Reviewerly/exHarmony huggingface dataset:
Collaborative Learning Of Semi-Supervised Clustering And Classification For Labeling Uncurated Data

Domain-specific image collections present potential value in various areas of science and business but are often not curated nor have any way to readily extract relevant content. To employ contemporary supervised image analysis methods on such image data, they must first be cleaned and organized, and then manually labeled for the nomenclature employed in the specific domain, which is a time consuming and expensive endeavor. To address this issue, we designed and implemented the Plud system. Plud provides an iterative semi-supervised workflow to minimize the effort spent by an expert and, due to the fact that it does not make any assumption about its input, can handle realistic large collections of images regardless of their size and type. Plud is an iterative sequence of unsupervised clustering, human assistance, and supervised classification. With each iteration 1) the labeled dataset grows, 2) the generality of the classification method and its accuracy increases, and 3) manual effort is reduced. We evaluated the effectiveness of our system, by applying it on over a million images documenting human decomposition. In our experiment comparing manual labeling with labeling conducted with the support of Plud, we found that it reduces the time needed to label data and produces highly accurate models for this new domain.
Cybersecurity analysis using the blockchain algorithm in nuclear power plants to enhance safe operations

The blockchain technology has been applied to nuclear power plants in which the cyber performance using the security analysis information is highly enhanced. Following this study, other kinds of information in the nuclear stuff could be applied using the blockchain technology. The simulations are performed as the conventional with system dynamics (SD) and blockchain modified modeling. The blockchain quantifications are done for conventional algorithm and SD as the highest value is 2.0. In addition, the encryption analysis is done, which is obtained by the hash code with the modulus algorithm that the remainder is calculated. For example, in the case of Long-term Conduction, the highest value is 0.6. After taking the information of hash code, the decoding could be accomplished. The networking bodies have same information and can take the secured information.
Review Spam Detection Based on Multi-dimensional Features

Review spam detection aims to detect the reviews with false information posted by the spammers on social media. The existing methods of review spam detection ignore the importance of the information hidden in the user interactive behaviors and fail to extract the indistinct contextual features caused by irregular writing style of reviews. In this paper, a new review spam detection method based on multi-dimensional features is proposed. The method utilizes the principal component analysis to get low-dimensional features to characterize the user-product relationship. Then, a neural network constructed with nested LSTM and capsule network is trained to extract textual context features and spatial structure features. Finally, the model combines the text and user behavioral features as the overall features, which are used as the input to the classification module to detect spam reviews. Experimental results show that the F1 value of our proposed method is 1.6%~3.5% higher than the existing methods, indicating the efficiency and effectiveness of our model, especially on the natural distribution datasets.
ISLAMIC PHILOSOPHY AND ARTIFICIAL INTELLIGENCE: EPISTEMOLOGICAL ARGUMENTS

Abstract This essay presents an analysis of different processes of machine learning: supervised, unsupervised, and semisupervised, through the prism of the epistemologies of several prominent Islamic philosophical schools. I discuss the way each school conceptualizes the ontological absolute (immortality, death, afterlife) and the way this shapes their respective epistemologies. I present an analysis of the different machine learning processes through the prism of the epistemological constructs of each of these philosophic traditions. I conclude with the argument that more scholars from the Islamic philosophical tradition should engage in the debates about the development of artificial intelligence and its implications, given that many Muslim countries are among the leaders in this development and its application in everyday life.
Scene Text Recognition by Attention Network with Gated Embedding

Recurrent attention based encoder-decoder model is one of the most popular frameworks for scene text recognition. However, most methods in this category only use standard recurrent attention network as the decoder. In this paper, in order to alleviate the problem that standard attention network relies on the previous output character overmuch, we propose an attention network with gated embedding for scene text recognition. The proposed attention network with gated embedding (GEAN) adopts a gated embedding to adaptively reset the input information from the embedding vector of previous output character for recurrent attention network. The gated embedding is constructed by adding an adaptive embedding gate based on the degree of correlation between the hidden state vector and the embedding vector of the corresponding character at the same time step. We verify the effectiveness of GEAN for scene text recognition through extensive experiments on both regular and irregular scene text datasets. The performance of GEAN is shown to be superior to the standard recurrent attention based decoder and is comparable compared with state-of-the-art methods.
An improved algorithm for mining media content application patterns based on QPop increasing disk time domain segmentation and upgrading1

The intelligent scheduling algorithm for hierarchical data migration is a key issue in data management. Mass media content platforms and the discovery of content object usage patterns is the basic schedule of data migration. We add QPop, the dimensionality reduction result of media content usage logs, as content objects for discovering usage patterns. On this basis, a clustering algorithm QPop is proposed to increase the time segmentation, thereby improving the mining performance. We hired the standard C-means algorithm as the clustering core and used segmentation to conduct an experimental mining process to collect the ted QPop increments in practical applications. The results show that the improved algorithm has good robustness in cluster cohesion and other indicators, slightly better than the basic model.
Solving Engineering Optimization Problems Without Penalty

This paper presents a constraint handling approach without the need to use a penalty function, namely Optimization Without Penalty (OWP). This approach is used on a recent optimization algorithm based on morphological filters, called Optimization by Morphological Filters (OMF). This work consists of providing a simple and effective constraint-handling approach without the necessity of the penalty for any considered optimization problem. The proposed approach is applied to 03 hard mathematical functions, 18 benchmark constraint functions presented in CEC 2010 competition on constrained real-parameter optimization, and 7 constrained engineering design problems reported in the specialized literature. According to the statistical comparison results, the performance of the OMF algorithm with OWP-based approach is better or highly competitive on most of the studied problems.
Digital Image Forensics-Image Verification Techniques

In the present scenario of the digital era, we are on the brink of marginal transformation in digital imaging technology. Developments of high computational and artificial intelligence techniques have produced wonderful image editing techniques that create imposing results in stint frames. And, doing this will affect the original artifacts present in digital images. This refers to the manipulation or forgery of digital data with the help of image editing applications and this also erodes our trust in digital images. To rely on the semantics of digital images, there is significant research contribution in terms of various image forgery detection techniques. These techniques help to re-evaluate the authenticity of digital images. In this article, an examination of various research contributions is conducted. The primary goal of this work is to give a glimpse of various current existing techniques related to digital image forensics. These techniques are assessed as per their proficiencies and boundaries. Whenever possible, a comparison between similar domain techniques is presented. The main focus is on pixel and physics-based techniques. Our analysis discusses the challenges that are to be addressed in this area of forensic science and provides insight into various datasets available for researchers to develop and test new approaches. Finally, the work recommends possible future research directions.
Maximum Entropy Reinforcement Learning with Evolution Strategies

Evolution strategies (ES) have recently raised attention in solving challenging tasks with low computation costs and high scalability. However, it is well-known that evolution strategies reinforcement learning (RL) methods suffer from low stability. Without careful consideration, ES methods are sensitive to local optima and are unstable in learning. Therefore, there is an urgent need for improving the stability of ES methods in solving RL problems. In this paper, we propose a simple yet efficient ES method to stabilize the learning. Specifically, we propose a framework to incorporate the maximum entropy reinforcement learning with evolution strategies and derive an efficient entropy calculation method for linear policies. We further present a practical algorithm called maximum entropy evolution policy search based on the proposed framework, which is efficient and stable for policy search in continuous control. Our algorithm shows high stability across different random seeds and can obtain comparable results in performance against some existing derivative-free RL methods on several of the well-known benchmark MuJoCo robotic control tasks.
Memory Efficient Architecture for Lifting-Based Discrete Wavelet Packet Transform

This brief presents a novel VLSI architecture for computing discrete wavelet packet transform (DWPT) for the continuous flow of data. Each stage of the proposed multi-stage architecture consists of the bit-reordering circuit and serial wavelet filter. The conventional wavelet filter has been modified for single path serial data. The intermediate coefficients are reordered with the help of a bit reordering circuit in order to maintain a continuous flow of data from input to output end with minimum memory and minimum latency. In comparison to the recently published architectures, the proposed one not only reduces the memory requirement by more than 50% but also achieves a 100% hardware utilization ratio. Furthermore, the area and power requirements are reduced by more than 60% and 50%, respectively.
Layer-wise Adversarial Training Approach to Improve Adversarial Robustness

Deep neural networks (DNNs) have delivered state-of-the-art performance in many challenging tasks, such as in computer vision, but they are vulnerable to adversarial attacks. Adversarial training is a technique for augmenting training data with adversarial examples and has empirically proven to be the most effective method of defense against adversarial attacks. Motivated by the fact that intermediate layers play a highly important role in maintaining a robust model, we propose to extend conventional adversarial training, which is designed to only manipulate input, such that it becomes layer-wise training. Distinct from previous studies in which robust DNN models were trained in a layer-wise manner, the layer perturbation introduced by our method theoretically proves to be equivalent to the adversarial manipulation of network inputs. This approach guarantees an improvement in the adversarial robustness of DNN models to which the method is applied. We empirically evaluated both shallow and deep CNN models, such as VGG16 and WideResNet28-10, by using MNIST, CIFAR-10, and CIFAR-100 datasets. The results consistently showed that the proposed layer-wise adversarial training approach significantly outperforms conventional adversarial training and that it offers defense against all mainstream attacks including FGSM, IFGSM, PGD, EoT, and C&W. Combining the layer-wise training regime with conventional adversarial training would make it possible to achieve excellent defense performance.
Performance Impact of IEEE 802.3ad in Container-Based Clouds for HPC Applications

Historically, large computational clusters have supported hardware requirements for executing High-Performance Computing (HPC) applications. This model has become out of date due to the high costs of maintaining and updating these infrastructures. Currently, computing resources are delivered as a service because of the cloud computing paradigm. In this way, we witnessed consistent efforts to migrate HPC applications to the cloud. However, if on the one hand cloud computing offers an attractive environment for HPC, benefiting from the pay-per-use model and on-demand resource allocation, on the other, there are still significant performance challenges to be addressed, such as the known network bottleneck. In this article, we evaluate the use of a Network Interface Cards (NIC) aggregation approach, using the IEEE 802.3ad standard to improve the performance of representative HPC applications executed in LXD container based-cloud. We assessed the aggregation impact using two and four NICs with three distinct transmission hash policies. Our results demonstrated that if the correct hash policy is selected, the NIC aggregation can significantly improve the performance of network-intensive HPC applications by up to \(\approx \)40%.
Computing Autotopism Groups of Partial Latin Rectangles

Computing the autotopism group of a partial Latin rectangle (PLR) can be performed in multiple ways. This study has two aims: comparing some of these methods experimentally to identify those that are competitive; and identifying design goals for developing practical software. We compare six families of algorithms (two backtracking and four graph-theoretic methods), with and without using entry invariants (EIs), in a range of settings. Two EIs are considered: frequencies of row, column, and symbol representatives; and 2 × 2 submatrices. The best approach to computing autotopism groups varies. When PLRs have many autotopisms (such as having very few entries or being a group table), the McKay, Meynert, and Myrvold (MMM) method computes generators for the autotopism group efficiently. (The MMM method is the standard way to compute autotopisms.) Otherwise, PLRs ordinarily have trivial or small autotopism groups, and the task is to verify this. The so-called PLR graph method is slightly more efficient in this setting than the MMM method (in some circumstances, around twice as fast). With an intermediate number of entries, the quick-to-compute strong EIs are effective at reducing the need for computation without introducing significant overhead. With a full or almost-full PLR, a more sophisticated EI is needed to reduce down-the-line computation. These results suggest a hybrid approach to computing autotopism groups: The software decides on suitable EIs based on the input; and the user chooses between the MMM or the PLR graph methods, depending on their dataset. This article expands the authors’ previous article Computing autotopism groups of PLRs: a pilot study .
Self-Optimisation of Dense Neural Network Architectures: An Incremental Approach

This paper presents a newly developed self-structuring algorithm for generating convolutional neural networks, as well as the results of preliminary tests performed on it. The algorithm produces DenseNet and DenseNet-BC architectures layer by layer from scratch, at the same time as they are being trained. Experimental results for well-known image classification datasets (CIFAR-10 and SVHN) are promising. The accuracy levels of generated networks are not significantly different than those of prebuilt DenseNet and DenseNet-BC with similar topologies, and are approaching the state of the art for these datasets.
Adaptive Inner-reward Shaping in Sparse Reward Games

Reinforcement learning focuses on goal-directed learning from interaction and the success of its applications strongly depends on how well the reward signal frames the problem and how well it assesses progress in solving it. But in many real-world scenarios, the agent is supplied with extremely sparse or even no rewards which makes learning fail and fall into ineffective exploration. In psychology, shaping is a method of animal training by reinforcing successive approximations of rewards to finally achieve the desired complex behavior. Inspired by this phenomenon of animal learning and reward as a signal in neuroscience, in this paper we solve the sparse reward problem by constructing a reward generator to generate inner-rewards and guide the agent learning control policies with deep neural networks. The proposed learning-based reward shaping does not require specific domain knowledge, but rather enable the agent to learn how to generate inner rewards to guide itself in any scenarios online jointly with the actual reinforcement learning process. To validate the performance in complex sparse reward problems, the proposed approach is evaluated in a challenging scenario, Football Academy in Google Research Football Environment, a newly released reinforcement learning environment with physics-based 3D simulator, instead of maze environments or grid world that are commonly used in research which are not sufficiently challenging. We compare the performance of our inner-rewards approach with two reinforcement algorithms (PPO and ICM + PPO). Experimental results show that our method improves the learning performance in terms of speed and quality, and also enables the agent to learn generalized skills applied to novel scenarios.
Cross Message Passing Graph Neural Network

Most Graph Convolutional Networks (GCNs) used for graph classification task can fit into the Message Passing Neural Networks (MPNNs) framework. However, traditional MPNNs don't consider global information in the message passing phase in which the node embeddings are updated. In this paper, we propose a new model called Cross Message Passing Graph Neural Network (CMPGNN). The new model consists of two message passing phases, of which one is the local message passing phase used for node embeddings updating and another is the global message passing phase used for graph feature updating. Several convolutional layers are stacked together in the local message passing phase, in which different convolutional layers are used to update the node embeddings at different time steps. Each convolutional layer updates node embeddings not only according to the outputs calculated at the previous convolutional layer but also to the graph feature calculated at the previous time step. A readout layer shared by all time steps is used in the global message passing phase. At each time step, after the node embeddings are updated, the readout layer aggregates the embeddings of all nodes by using a global gated network, and feeds the aggregation result into a Gated Recurrent Unit (GRU) to update the graph feature.The above two message passing phases are executed alternatively. After all time steps, the graph feature obtained by the readout layer is fed into a MultiLayer perception for graph classification. We evaluate the performance of CMPGNN on 6 graph classification datasets. Experimental results show that compared with other 10 baselines, CMPGNN achieves the highest accuracy on 4 of the 6 benchmark datasets.
Word-Character Hybrid Machine Translation Model

In this paper, we propose a method which combines word and character attention information to improve neural machine translation. For this, two attention mechanisms are used. Firstly, with the help of Gated Recurrent Unit character-level attention has taken input as sequence of characters. After that, word-level pays attention to the words composed from character-level attention. With the fusion of two attentions, encoder encodes information simultaneously. On the other side, decoder decodes the information based on word-level only. The experimental results proved the proposed method outperformed the baseline system for English-Hindi language pair.
Air Data Sensor Fault Detection and Diagnosis in the Presence of Atmospheric Turbulence: Theory and Experimental Validation With Real Flight Data

Managing air data sensor fault detection and diagnosis (FDD) in the presence of atmospheric turbulence is challenging since the effects of faults and turbulence are coupled. Existing FDD approaches cannot decouple the faults from the turbulence. To address this challenge, this brief first proposes a novel kinematic model that incorporates the effects of the turbulence. This model is valid inside the entire flight envelope, and there is no need to design a linear parameter varying system. Then, the double-model adaptive estimation algorithm is extended to achieve unbiased state estimation even in the presence of unknown disturbances. The proposed approach is validated using generated turbulence data with various scale lengths and intensities. More importantly, the proposed approach is successfully validated using the real flight test data of a business jet when it is experiencing atmospheric turbulence.
Low-Power and Area-Efficient Finite Field Multiplier Architecture Based on Irreducible All-One Polynomials

This paper presents a low-power and area-efficient finite field multiplier based on irreducible all-one polynomials (AOP). The proposed architecture implements the AOP multiplication algorithm in three stages, which are reduction network, AND network (multiplication), and three input XOR tree (accumulation), while state-of-the-art implementations distribute reduction, multiplication and accumulation operations in a systolic array. The optimization reduces the overall number of sequential instances and provides lower pipeline latency compared to literature. This leads to the reduction of power dissipation and area for a targeted system clock frequency. Both the previously reported and the proposed architectures have been implemented in Verilog for three different and relevant binary field sizes using TSMC 130-nm standard cell library from Artisan Components, and have been synthesized with a 100 MHz system clock frequency target using the Cadence Genus Synthesis tool. The proposed architecture offers 23%, 41%, and 20% reduction in average leakage, dynamic power, and area, respectively, compared to state of the art. The pipelined latency advantage can be translated to further power dissipation reduction by targeting a lower clock frequency in applications where single AOP multiplication execution time is more important than pipeline throughput. Thus, the proposed architecture is better suited for energy-efficient portable systems, including wireless sensors.
The Principles of Organizing University Networking Cooperation

This electronic document deals with the principles of organizing university networking cooperation, the main characteristics of networking organizations are given, the examples of the networking cooperation based on Ryazan State Radio Engineering University named after V.F. Utkin (Russia) are discussed.