Reviewerly/exHarmony huggingface dataset:
Federated Learning Over Wireless Channels: Dynamic Resource Allocation and Task Scheduling

With the development of federated learning (FL), mobile devices (MDs) are able to train their local models with private data and send them to a central server for aggregation, thereby preventing leakage of sensitive raw data. In this paper, we aim to improve the training performance of FL systems in the context of wireless channels and stochastic energy arrivals of each MD. To this purpose, we dynamically optimize MDs’ transmission power and training task scheduling. We first model this dynamic programming problem as a constrained Markov decision process (CMDP). Due to high dimensions of the proposed CMDP problem, we propose online stochastic learning methods to simplify the CMDP and design online algorithms to obtain an efficient policy for all MDs. Since there are long-term constraints in our CMDP, we utilize a Lagrange multipliers approach to tackle this issue. Furthermore, we prove the convergence of the proposed online stochastic learning algorithm. Numerical results indicate that the proposed algorithms can achieve better performance than the benchmark algorithms.
Trusted Inter-Process Communication Using Hardware Enclaves

Inter-Process Communication (IPC) enables applications to share information in a local or distributed environment, allowing them to communicate with each other in a coordinated manner. In modern systems this mechanism is extremely important, as even local applications can run parallel tasks in multiple processes in the machine, needing to exchange information to coordinate their execution, and optimizing the exchange of data in a more efficient way. The security in IPC relies on the integrity and confidentiality of the messages exchanged in such an environment, as messages exchanged between different processes can be targeted by attacks that seek to obtain sensitive data or to manipulate the application behavior. A Trusted Execution Environment (TEE) can be used to enable an isolated execution of the IPC mechanism to mitigate such attacks. In this paper we propose the adoption of the Intel Software Guard Extensions (SGX) architecture to provide data confidentiality and integrity in message exchange between processes, by using hardware instructions and primitives to encrypt and authenticate the messages. Our approach highlights a threat model and compares the solution proposed with two other scenarios, showing a feasible solution for security and an approach that can be applied to standard IPC mechanisms with small processing overhead.
A Quantum-Based Blockchain Approach to Voting Protocol Using Hyperledger Sawtooth

Abstract Protection measures are essential to present day blockchain innovation ever, since they can exist short of empowered outsider, which implies that there may not be a disclosed trustworthy individual or group responsible for frameworks. Security of the present frameworks depends on estimating the firmness assumptions and large numbers of the benchmark cryptographic functions proven to be powerless for crucial monetary and a variety of applications against the approach of undeniable quantum machines. Upgrading blockchain innovation with the future of quantum states in a shared manner will enhance the degree of protection and security by-laws of physical science, which is never feasible from non-quantum data hypothetical perspectives. In this article, we propose a quantum-built way to deal with harness of security for a democratic application with the execution, utilizing Hyperledger Sawtooth.
A Consortium Medical Blockchain Data Storage and Sharing Model Based on IPFS

As an important information resource, patients' medical data can not only help them to monitor their physical conditions and check the hidden risks of diseases;it an also provide strong evidence for doctor-patient disputes and promote the development of medical field.However, most medical institutions use centralized servers to centrally manage patients' electronic medical data.Such centralized servers have opaque data storage and are prone to suffer from single point of failure,leading to easy tampering or even deletion of medical data,At the same time, different standards of data management among medical institutions make it difficult to circulate and share data among institutions.The purpose of this paper is to realize efficient data sharing and patient access control of medical data under the condition of guaranteeing the safe storage of bulk medical data.Combining decentralized blockchain technology with distributed file storage platform IPFS, a federated medical blockchain data storage access model based on IPFS is proposed.The model uses the multi-privilege attribute-base encryption algorithm (MA-ABE) to encrypt the data to ensure the safety of the patient's medical data during transmission and the patient's ability to control their own medical data.Meanwhile the private IPFS network is built to improve the upload and download rate of encrypted data.This paper introduces the components and principles of the model and elaborates the flow of data upload and access.In the mean time, evaluate the performance of the model, summarize the advantages and disadvantages of the model and the next research of the paper.
Obfuscated Tor Traffic Identification Based on Sliding Window

Tor is an anonymous communication network used to hide the identities of both parties in communication. Apart from those who want to browse the web anonymously using Tor for a benign purpose, criminals can use Tor for criminal activities. It is recognized that Tor is easily intercepted by the censorship mechanism, so it uses a series of obfuscation mechanisms to avoid censorship, such as Meek, Format-Transforming Encryption (FTE), and Obfs4. In order to detect Tor traffic, we collect three kinds of obfuscated Tor traffic and then use a sliding window to extract 12 features from the stream according to the five-tuple, including the packet length, packet arrival time interval, and the proportion of the number of bytes sent and received. And finally, we use XGBoost, Random Forest, and other machine learning algorithms to identify obfuscated Tor traffic and its types. Our work provides a feasible method for countering obfuscated Tor network, which can identify the three kinds of obfuscated Tor traffic and achieve about 99% precision rate and recall rate.
Robust multi-view fuzzy clustering via softmin

Multi-view clustering, which utilizes the ample information provided by multiple sources to obtain better performance, has attracted much attention. However, existing clustering algorithms either have no ability to offer confidence for each assignment or suffer from the disturbance of outliers. To address these problems, in this paper, we propose a novel multi-view fuzzy clustering method via transferring softmin to fuzzy models. To obtain fuzzy assignments, we utilize the softmin with temperature and further develop an efficient algorithm to solve the non-convex problem approximately. We also show another explanation for the algorithm from the aspect of the prior distribution of various views. Besides, we design a scalable robust loss function, which interpolates between ℓ2-norm and the squared ℓ2-norm, to enhance the robustness to outliers. Extensive experiments show the superiority of our model under different clustering metrics.
Facial Features Detection: A Comparative Study

Facial feature detection is an essential step in many human face-related applications including facial expression analysis, 3D face reconstruction, pose normalization, vision-based biometrics, and face recognition/verification. Though facial features detection or fiducial facial landmark points may seem an easy process for humans, it faces some challenges as the selection accuracy varies depending on some factors, such as pose, expression, illumination, and occlusion. Several facial landmark detection methods have been introduced to automatically detect those key points over the last ten years. In this paper, a comparative study is conducted to evaluate the efficiency of the state-of-the-art methods used for facial features detection. Seven fiducial points are considered in this study, namely left eye left corner, left eye right corner, right eye left corner, right eye right corner, left mouth corner, right mouth corner, and the tip of the nose. The experiments are conducted on three widely-adopted challenging datasets: BioID, AFLW, and 300W. The experimental results obtained on these three benchmark datasets show that the robustness can be further improved for detection algorithms and reveal future avenues for further research on the topic.
Neural Abstractive Unsupervised Summarization of Online News Discussions

Summarization has usually relied on gold standard summaries to train extractive or abstractive models. Social media brings a hurdle to summarization techniques since it requires addressing a multi-document multi-author approach. We address this challenging task by introducing a novel method that generates abstractive summaries of online news discussions. Our method extends a BERT-based architecture, including an attention encoding that fed comments' likes during the training stage. To train our model, we define a task which consists of reconstructing high impact comments based on popularity (likes). Accordingly, our model learns to summarize online discussions based on their most relevant comments. Our novel approach provides a summary that represents the most relevant aspects of a news item that users comment on, incorporating the social context as a source of information to summarize texts in online social networks. Our model is evaluated using ROUGE scores between the generated summary and each comment on the thread. Our model, including the social attention encoding, significantly outperforms both extractive and abstractive summarization methods based on such evaluation.
Online Resource Auction for EAVN With Non-Price Attributes

Through offloading tasks to surrounding edge nodes, the edge-assistant vehicular network (EAVN) could provide faster and more efficient services. To promote the deployment of EAVN, we need a reasonable resource allocation scheme. Nowadays, there are few auction mechanisms in EAVN scenario. Moreover, the auction mechanism in other edge computing scenario has following limitations. Firstly, current works only focus on the static/offline auction and do not consider that users will join and leave the auction system at any time in EAVN. Secondly, current works that determine the winning buyers/sellers only based on the price do not consider the unique attributes of EAVN, such as poor communication quality and various task demands. We design an online auction scheme for EAVN, which could satisfy the dynamics of users in the auction system. Moreover, the proposed auction mechanism also considers the non-price attributes, such as location, reputation, and computing ability, when constructing the matching between buyers and sellers. To verify our work, we simulate EAVN using a vehicular network simulator. Experimental results show that the proposed mechanism could meet the properties of computational efficiency, individual rationality, budget balance, and truthfulness.
Transferable Adversarial Examples for Anchor Free Object Detection

Deep neural networks have been demonstrated to be vulnerable to adversarial attacks: subtle perturbation can completely change prediction result. The vulnerability has led to a surge of research in this direction, including adversarial attacks on object detection networks. However, previous studies are dedicated to attacking anchor-based object detectors. In this paper, we present the first adversarial attack on anchor-free object detectors. It conducts category-wise, instead of previously instance-wise, attacks on object detectors, and leverages high-level semantic information to efficiently generate transferable adversarial examples, which can also be transferred to attack other object detectors, even anchor-based detectors such as Faster R-CNN. Experimental results on two benchmark datasets demonstrate that our proposed method achieves state-of-the-art performance and transferability.
Variational policy search using sparse Gaussian process priors for learning multimodal optimal actions

Policy search reinforcement learning has been drawing much attention as a method of learning a robot control policy. In particular, policy search using such non-parametric policies as Gaussian process regression can learn optimal actions with high-dimensional and redundant sensors as input. However, previous methods implicitly assume that the optimal action becomes unique for each state. This assumption can severely limit such practical applications as robot manipulations since designing a reward function that appears in only one optimal action for complex tasks is difficult. The previous methods might have caused critical performance deterioration because the typical non-parametric policies cannot capture the optimal actions due to their unimodality. We propose novel approaches in non-parametric policy searches with multiple optimal actions and offer two different algorithms commonly based on a sparse Gaussian process prior and variational Bayesian inference. The following are the key ideas: (1) multimodality for capturing multiple optimal actions and (2) mode-seeking for capturing one optimal action by ignoring the others. First, we propose a multimodal sparse Gaussian process policy search that uses multiple overlapped GPs as a prior. Second, we propose a mode-seeking sparse Gaussian process policy search that uses the student-t distribution for a likelihood function. The effectiveness of those algorithms is demonstrated through applications to object manipulation tasks with multiple optimal actions in simulations.
Neural network interpolation operators activated by smooth ramp functions

In this paper, we introduce some neural network interpolation operators activated by smooth ramp functions. By using the smoothness of the ramp functions, we can give some useful estimates of the derivatives of the neural networks, which combining with some techniques in approximation theory enable us to establish the converse estimates of approximation by neural networks. We establish both the direct and the converse results of approximation by the new neural network operators defined by us, and thus give the essential approximation rate. To improve the approximation rate for functions of smoothness, we further introduce linear combinations of the new operators. The new combinations interpolate the objective function and its derivative. We also estimate the uniform convergence rate and simultaneous approximation rate by the new combinations.
Oversampling Errors in Multimodal Medical Imaging Are Due to the Gibbs Effect

To analyze multimodal three-dimensional medical images, interpolation is required for resampling which—unavoidably—introduces an interpolation error. In this work we describe the interpolation method used for imaging and neuroimaging and we characterize the Gibbs effect occurring when using such methods. In the experimental section we consider three segmented three-dimensional images resampled with three different neuroimaging software tools for comparing undersampling and oversampling strategies and to identify where the oversampling error lies. The experimental results indicate that undersampling to the lowest image size is advantageous in terms of mean value per segment errors and that the oversampling error is larger where the gradient is steeper, showing a Gibbs effect.
Fruit-fly Inspired Neighborhood Encoding for Classification

Inspired by the fruit-fly olfactory circuit, the Fly Bloom Filter is able to efficiently summarize the data with a single pass and has been used for novelty detection. We propose a new classifier that effectively encodes the different local neighborhoods for each class with a per-class Fly Bloom Filter. The inference on test data requires an efficient Flyhash[6] operation followed by a high-dimensional, but very sparse, dot product with the per-class Bloom Filters. On the theoretical side, we establish conditions under which the predictions of our proposed classifier agrees with the predictions of the nearest neighbor classifier. We extensively evaluate our proposed scheme with 71 data sets of varied data dimensionality to demonstrate that the predictive performance of our proposed neuroscience inspired classifier is competitive to the nearest-neighbor classifiers and other single-pass classifiers.
Coupling local and nonlocal diffusion equations for image denoising

Due to the strong ability of restoring textures and details in images, nonlocal equations have attracted an extensive interest for image denoising. However, the lack of regularity causes residual noise in the restored images. In this paper, we propose and study an evolution equation consisting of the weighted local and the weighted nonlocal p-Laplacian equations. The existence and uniqueness of solutions for the proposed equation are proven under the assumption that the weights vanish in sets of positive measure. We also show that solutions of the proposed equation converge to the solution of the usual p-Laplacian equation if the kernel is rescaled appropriately. Comparisons with local and nonlocal diffusion equations for removing Gaussian noise in images are presented.
A fast Binary Decision Diagram (BDD)-based reversible logic optimization engine driven by recent meta-heuristic reordering algorithms

Reversible logic has recently gained a remarkable interest due to its information lossless property, which minimizes power dissipation in the circuit. Furthermore, with their natural reversibility, quantum computations can profit from the advances in reversible logic synthesis, as the latter can be easily applied to map practical logic designs to quantum architectures. Although numerous algorithms have been proposed to synthesize reversible circuits with low cost, the increasing demands for scalable synthesis techniques represent a serious barrier in the synthesis process. Furthermore, the enhanced reliability of the synthesized circuits comes at the cost of redundancy in the quantum architecture of the gates composing that circuit, which increases the overall manufacturing cost for fault-tolerant circuits. Binary Decision Diagram (BDD) based synthesis has demonstrated a great evidence in reversible logic synthesis, due to its scalability in synthesizing complex circuits within a reasonable time. However, the cost of the synthesized circuit is roughly correlated to its corresponding BDD size. In this paper, we propose a fast reversible circuit synthesis methodology driven by a BDD-reordering optimization engine implemented by recent meta-heuristic optimization algorithms. Experimental results show that Genetic Algorithm (GA) based reordering supported with Alternating Crossover (AX) and swap mutation outperforms others as it is the least destructive for low-cost BDDs during the optimization recipe.
Smart Online Voting System

Our country, India is the largest democratic country in the world. So it is essential to make sure that the governing body is elected through a fair election. India has only offline voting system which is not effective and upto the mark as it requires large man force and it also requires more time to process and publish the results. Therefore, to be made effective, the system needs a change, which overcomes these disadvantages. The new method does not force the person's physical appearance to vote, which makes the things easier. This paper focusses on a system where the user can vote remotely from anywhere using his/her computer or mobile phone and doesn't require the voter to got to the polling station through two step authentication of face recognition and OTP system. This project also allows the user to vote offline as well if he/she feels that is comfortable. The face scanning system is used to record the voters face prior to the election and is useful at the time of voting. The offline voting system is improvised with the help of RFID tags instead of voter id. This system also enables the user the citizens to see the results anytime which can avoid situations that pave way to vote tampering.
The effects of publishing processes on scientific thought: Typography and typology in prehistoric archaeology (1950s–1990s)

In the last decades, many changes have occurred in scientific publishing, including online publication, data repositories, file formats and standards. The role played by computers in this process rekindled the argument on forms of technical determinism. This paper addresses this old debate by exploring the case of publishing processes in prehistoric archaeology during the second part of the twentieth century, prior to the wide-scale adoption of computers. It investigates the case of a collective and international attempt to standardize the typological analysis of prehistoric lithic objects, coined typologie analytique by Georges Laplace and developed by a group of French, Italian, and Spanish researchers. The aim of this paper is to: 1) present a general bibliometric scenario of prehistoric archaeology publishing in continental Europe; 2) report on the little-known typologie analytique method in archaeology, using publications, archives, and interviews; 3) show how the publication of scientific production was shaped by social (editorial policies, support networks) and material (typography features and publication formats) constraints; and 4) highlight how actors founded resources to control and counterbalance these effects, namely by changing and improving publishing formats.
Assessment2Vec: Learning Distributed Representations of Assessments to Reduce Marking Workload

Reducing instructors workload in online and large-scale learning environments could be one of the most important factors in educational systems. To address this challenge, techniques such as Artificial Intelligence has been considered in tutoring systems and automatic essay scoring tasks. In this paper, we construct a novel model to enable learning distributed representations of assessments namely Assessment2Vec and mark assessments automatically with Supervised Contrastive Learning loss which will effectively reduce instructors' workload in marking large number of assessments. The experimental results based on the real-world datasets show the effectiveness of the proposed approach.
Minimum variance-embedded kernelized extension of extreme learning machine for imbalance learning

In classification problems, detecting a skew class has extensively been studied in the machine learning community. Traditional extreme learning machine (ELM) algorithm becomes biased towards the majority class due to imbalance learning. To handle this problem, several extensions of ELM have been proposed such as variances-constrained weighted ELM (VW-ELM) and class-specific kernelized ELM (CSKELM). Kernelized ELM (KELM) has a better generalization capability than traditional ELM. This work proposes novel minimum variance embedded-kernelized weighted extreme learning machine (MVKWELM) and minimum variance-embedded class-specific kernelized extreme learning machine (MVCSKELM) methods for handling the imbalanced classification problems more effectively. These methods constitute novel extensions of the VW-ELM and CSKELM classifiers respectively. This minimum variance-embedding enhances the generalization capability of the algorithm by minimizing the intra-class variance. MVCSKELM uses the advantages of both the minimum variance-embedding framework and the class-specific regularization parameters. The proposed MVCSKELM also has comparable computational complexity compared to kernelized weighted ELM (KWELM). The proposed MVCSKELM adopted class-specific regularization parameters, which are determined by using class distribution. The proposed works are evaluated using benchmark real-world imbalanced datasets downloaded from the KEEL dataset repository. The experimental results demonstrate that MVKWELM and MVCSKELM achieve superior performance in contrast to KELM, KWELM, CCR-KELM, CSKELM, RUSBoost, WKSMOTE, VW-ELM, and EasyEnsemble for imbalance learning.