Reviewerly/exHarmony huggingface dataset:
Convolutional neural networks with coherent nanophotonic circuits

Convolutional neural networks (CNN) has significant advantages in processing image classification and was widely used in image analysis in the fields of autonomous driving, aerospace, and biomedicine. However, image classification and analysis need large matrix multiplication, which imposes many challenges to the realization of high performance and low power consumption of CNNS. Here, a photoelectric hybrid neural network (PHNN) was developed to reduce the CNN’s power consumption. The optical interference unit (OIU) composed of Mach-Zehnder interferometers (MZI) arrays, used as convolution kernel, performs multiplication and accumulation operations. The convolution kernel is split and reorganized effectively to form a new unitary matrix to reduce the number of MZIs. Simultaneously, this method can modularize the OIU, which is beneficial to field-programmable gate array (FPGA) encoding and modulation. FPGA realizes nonlinear calculation, data scheduling and storage, and phase encoding and modulation. Our PHNN has an accuracy rate of 93.3%, which reduces power consumption by 3 times of magnitude compared with traditional electronic products.
On the Effect of Data Centering on Spectral Clustering with Cosine Similarity

Data clustering, i.e., the process of dividing unlabeled data into meaningful groups, is one of the most fundamental mechanisms of machine learning. Spectral Clustering is an important clustering technique and has a well-established and rich history in various scientific fields since 1980s. In various applications, Squared-Euclidean distance and cosine distance (angular distance) are used in Spectral Clustering both of which provide us with a different aspect of similarity between any two data points. Before applying a Spectral Clustering algorithm, the data can be pre-processed. Among different pre-processing procedures, one of the most common operations is the well-known mean centering. In this paper, we address the effects of a general data-centering procedure onto the spectral features of corresponding Laplacian matrix for the cosine distance case, and we pose a novel optimum data normalization problem from spectral clustering point of view. Computer simulations of some simple examples suggest that data centering with normalization has a good potential to improve the spectral clustering performance.
Simulation and verification in high-performance computing for cluster distributed doubly fed induction generators in the horizon of Ecological Marxism

Facing the global ecological crisis, a feasible solution is the technical solution, that is, to replace the large-scale and centralized technical model under the capital logic with a small-scale, decentralized and diversified technical model in the horizon of Ecological Marxism. In reality, it is a decentralized wind power generation mode based on clean energy to replace the centralized large-scale traditional energy. In order to reduce computational complexity of massive DFIGs group in large power grid simulation, decentralized algorithm and architecture used in High-Performance Computing (HPC) are proposed and verified in this paper, which can batch automatic allocation of models to different computing units for parallel computing, and the fine-grained decoupling distributed parallel calculation method can effectively solve the exponential explosion of computing dimensions. The result waveforms under steady-state and three-phase fault transient conditions are basically consistent, which can meet the requirements of calculation accuracy, and also verified the efficiency of paper’s work.
Development of a System for Using Transcranial Doppler Monitoring with Virtual Reality Head Mounted Displays

New head mounted display devices for virtual reality (VR) have appeared in the last years and have made this technology closer to the population, allowing its wide diffusion in the mass market. Currently, VR is a tool with applications in many areas, including motor training and rehabilitation. In many of these applications, it is mandatory to evaluate participants’ brain responses during the performance of tasks in VR. One of the techniques that has been applied in psychophysiological research for this purpose is Transcranial Doppler (TCD), a non-invasive technique based on ultrasound that monitors blood flow velocity in main cerebral vessels. However, up to now, VR systems based on head mounted displays are not prepared for their simultaneous use with TCD monitoring. In the present work, a VR system based on Oculus Rift that allows a simultaneous recording of the TCD signal has been designed and developed. Special adapters have been proposed to allow the correct holding of the ultrasound probes to the VR head mounted display. It has been checked that the system allows a correct monitoring of the TCD signal during VR exposure.
Dog Breed Library with Picture-Based Search Using Neural Networks

In the past decade, there have been significant improvements in image classification thanks to approaches using neural networks. With the widespread availability of free and open-source frameworks for training neural networks, such as Google&#x0027;s TensorFlow, it is now feasible to make use of advanced image classification in novel areas. One such area is the problem of identifying dog breeds from photos. The American Kennel Club currently recognizes 193 distinct breeds-all of which not only look different and have different temperaments, but which may require special care, such as frequent grooming, a specific diet, or a more active lifestyle. Therefore, it is important for dog owners to know their dogs&#x0027; breeds. The main subject of this paper is the usage of neural networks to provide non-technical users with the means to automatically identify breeds of dogs from their pictures.
Representational Gradient Boosting: Backpropagation in the Space of Functions

The estimation of nested functions (i.e., functions of functions) is one of the central reasons for the success and popularity of machine learning. Today, artificial neural networks are the predominant class of algorithms in this area, known as representational learning. Here, we introduce Representational Gradient Boosting (RGB), a nonparametric algorithm that estimates functions with multi-layer architectures obtained using backpropagation in the space of functions. RGB does not need to assume a functional form in the nodes or output (e.g., linear models or rectified linear units), but rather estimates these transformations. RGB can be seen as an optimized stacking procedure where a meta algorithm learns how to combine different classes of functions (e.g., Neural Networks (NN) and Gradient Boosting (GB)), while building and optimizing them jointly in an attempt to compensate each other's weaknesses. This highlights a stark difference with current approaches to meta-learning that combine models only after they have been built independently. We showed that providing optimized stacking is one of the main advantages of RGB over current approaches. Additionally, due to the nested nature of RGB we also showed how it improves over GB in problems that have several high-order interactions. Finally, we investigate both theoretically and in practice the problem of recovering nested functions and the value of prior knowledge.
Rocky: Replicating Block Devices for Tamper and Failure Resistant Edge-based Virtualized Desktop Infrastructure

Recently, edge-based virtual desktop infrastructure (EdgeVDI), which brings the power of virtualized desktop infrastructure to cloudlets closer to users, has been considered as an attractive solution for WAN mobility. However, ransomware and wiper malware are becoming more and more prevalent, which can impose serious cybersecurity threats to EdgeVDI users. Existing tamper-resistant solutions cannot deal with cloudlet failures. In this paper, we propose Rocky, the first distributed replicated block device for EdgeVDI that can recover from tampering attacks and failures. The key enabler is replicating to store a consistent write sequence across cloudlets as an append-only immutable mutation history. In addition, Rocky uses a replication broker to allow heterogenous cloudlets to control replication rates at their pace and reduces both disk space and network bandwidth consumption by coalescing writes for both uplink and downlink. To show the feasibility of Rocky, we implemented Rocky in Java. The experimental results show that Rocky’s write and read throughputs are similar to those of a baseline device with 8.4% and 11.9% additional overheads, respectively. In addition, we could reduce repeated writes by 88.5% and 100% for editing presentation slides and a photo, respectively.
POI Recommendation Based on Graph Enhanced Attention GNN

With the continuous progress of mobile Internet technology and GPS positioning technology of mobile devices, Social Network and Location Based Services (LBS) are gradually converging to form Location Based Social Network (LBSN). POI (Point of Interest) recommendation systems face the problems of variable user interests, very sparse user and POI check-in matrices, and nonlinear interaction modeling. To address the above problems, a Graph-enhanced Attention Graph Neural Network model is proposed for POI recommendation (POI-GAGN in short). POI-GAGN mines user/POI node representations on user-POI interaction graph, user-user social interaction graph, and POI-POI association interaction graph through interaction node feature extraction module, learns POI attribute information representations through text feature extraction module, and extracts short-term preference representations of users through short-term preference extraction module. A graph-enhanced attention mechanism is designed to correlates node representations, attribute information representations of POI, and short-term preferences of users with each other to achieve better information fusion. Finally, we conduct sufficient experiments on two real datasets to prove that the recommendation effect of POI-GAGN is better than other current advanced POI recommendation methods, and POI-GAGN can better overcome the problems of data sparsity and cold start in recommendations.
Optimized Paillier’s Cryptosystem with Fast Encryption and Decryption

In this paper, we propose a new optimization for the Paillier’s additively homomorphic encryption scheme (Eurocrypt’99). At the heart of our optimization is a well-chosen subgroup of the underlying , which is used as the randomness space for masking messages during encryption. The size of the subgroup is significantly smaller than that of , leading to faster encryption and decryption algorithms of our optimization. We establish the one-wayness and semantic security of our optimized Paillier scheme upon those of an optimization (i.e., “Scheme 3”) made by Paillier in Eurocrypt’99. Thus, our optimized scheme is one-way under the partial discrete logarithm (PDL) assumption, and is semantically secure under the decisional PDL (DPDL) assumption. On the other hand, we present a detailed analysis on the concrete security of our optimized scheme under several known methods. To provide 112-bit security, our analysis suggests that a 2048-bit modulus N and a well-chosen subgroup of size 448-bit would suffice. We compare our optimization with existing optimized Paillier schemes, including the Jurik’s optimization proposed by Jurik in his Ph.D. thesis and the Paillier’s optimization in Eurocrypt’99. Our experiments show that, – the encryption of our optimization is about 2.7 times faster than that of the Jurik’s optimization and is about 7.5 times faster than that of the Paillier’s optimization
Towards Richer Assisted Living Environments

This paper describes an ongoing research project which explores the design and use of inexpensive robotics, artificial intelligence techniques, and human-computer interaction methods, to enrich assisted living environments. Such environments provide help to the inhabitants of a home or office, assisting them to perform daily activities, helping them to socialize and interact with others, and to provide enhanced levels of security and safeness. We present the development of an inexpensive robotic solution to help people with disabilities and/or older adults to perform their daily activities. It can be used as a remote controlled surveillance system, and also as a personal assistant. It is able to recognize each inhabitant, his/her emotions, and detect abnormal situations such as falls and health problems. The whole system is designed to operate solely within a local network and special attention is given to the privacy and data protection of the users.
Target-visible Polynomial Trajectory Generation within an MAV Team

Autonomous aerial videography is a challenging task, which involves collision avoidance against obstacles and visibility guaranteed target tracking in unstructured environments. In this paper, we organize a two micro aerial vehicle (MAV) team, which consists of a target agent responsible for a specific mission and a camera agent for filming the target agent. Especially, this paper focuses on trajectory planning of the camera agent to chase without occlusion of target agent. Our trajectory planner module includes two phases of guaranteeing target visibility. In the first phase, we generate homotopic safe flight corridor (SFC) to attain target-visible regions. In the subsequent phase, we generate a safe and smooth trajectory with the continuous visibility constraint based on the SFC, using quadratic programming (QP). Regardless of complexity of map, our planner converts an overall problem to a single QP and generates a steady flight trajectory without undesirable fluctuating motion, while guaranteeing all-time visibility. We validate our approach in Gazebo simulations and a real-world experiment.
GCOOD: A Generic Coupled Out-of-Distribution Detector for Robust Classification

Neural networks have achieved high degrees of accuracy in classification tasks. However, when an out-of-distribution (OOD) sample (i.e., entries from unknown classes) is submitted to the classification process, the result is the association of the sample to one or more of the trained classes with different degrees of confidence. If any of these confidence values are more significant than the user-defined threshold, the network will mislabel the sample, affecting the model credibility. The definition of the acceptance threshold itself is a sensitive issue in the face of the classifier’s overconfidence. This paper presents the Generic Coupled OOD Detector (GCOOD), a novel Convolutional Neural Network (CNN) tailored to detect whether an entry submitted to a trained classification model is an OOD sample for that model. From the analysis of the Softmax output of any classifier, our approach can indicate whether the resulting classification should be considered or not as a sample of some of the trained classes. To train our CNN, we had to develop a novel training strategy based on Voronoi diagrams of the location of representative entries in the latent space of the classification model and graph coloring. We evaluated our approach using ResNet, VGG, DenseNet, and SqueezeNet classifiers with images from the CIFAR-10 dataset.
D<sub>i</sub>PSVM: A Polynomial Kernel-free Support Vector Machine

Support Vector Machine (SVM), is a popular and efficient classification algorithm in machine learning (ML) paradigm. However, the kernel-based dependency of the SVM algorithm requires a long time to compute the support vectors for non-linear datasets. To remove the kernel, several types of functions are used with SVM. In earlier attempts, addition of kernel free approach in SVM caused major problems like repetitive feature space and long run time complexity. Therefore, a non-linear function is introduced in this study, namely i<sup>th</sup> degree polynomial (D<inf>i</inf>P). This function can directly identify non-linear features in a dataset. A kernel-free SVM model is proposed using D<inf>i</inf>P function named as D<inf>i</inf>PSVM in this paper. In D<inf>i</inf>PSVM, the input feature space is first taken into a new higher order feature space by using the multi-variable Taylor&#x2019;s expansion of the input features up to i<sup>th</sup> order to evaluate all the non-linear correlations among the input features. This method is implemented to check which specific ordered polynomial can increase the accuracy of the kernel free SVM model. Finally, sequential minimal optimization (SMO) is used for fast convergence to reduce the superiority of D<inf>i</inf>PSVM, which is demonstrated over ten benchmark categorical and continuous datasets obtained from UCI machine learning repository. Results revealed that D<inf>i</inf>PSVM gives better accuracy and faster convergence than kernel-based SVM algorithms.
Heuristic Guided Artificial Potential Field for Avoidance of Small Obstacles

In this paper, a modified heuristic guided Artificial Potential Field (APF) based algorithm has been proposed to find a practical trajectory for an Autonomous Unmanned Aerial Vehicle (UAV) path planning. The local minima are encountered in the conventional APF algorithm due to the cancellation of attractive and repulsive potential while avoiding unknown obstacles within the desired path, which results in the trapping of the agent before reaching the goal. Consequently, the traditional APF technique is therefore no longer advantageous in such cases. So in this proposed perpendicular approach based on APF helps to avoid such local minima. The advantage of the newly proposed method is the low computing time that lines up with the standard global path planner method. The proposed algorithm is tested and validated against existing general potential field techniques for different simulation scenarios in a 3D simulated environment using ROS and Gazebo supported PX4-SITL. The results have been presented for drone navigation and obstacle avoidance for the different scenarios in a simulated environment.
Comparison of Seven Empirical Models For Estimating Monthly Global Solar Radiation, (Case Study: Libya)

The aim of this study is to compare seven empirical models to find out which of them is the most efficient for estimating monthly global solar radiation. 3-years period data (2018-2020) used for the comparison were collected using automatic weather station installed at the Libyan Center for Solar Energy Research and Studies site, Tripoli, Libya (Lat. 32.815 N, Long. 13.439 E). The least square method is used to establish the models and determine their empirical coefficients. In order to evaluate the performance of the established models, three statistical error tests were introduced, root mean square error, RMSE, mean absolute percentage error, MAPE, and correlation coefficient, R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The obtained results indicated that the weather condition is fair during May-September, and scattered clouds during the other months. The results also indicated that the sunshine duration-based quadratic model presented the best efficiency for estimating the monthly average of daily global solar radiation on a horizontal surface, where it had the highest value of correlation coefficient, and the smallest values of MAPE and RMSE of 0.93, 2.8049, and 0.0195 kWh⁄m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ⁄day, respectively.
Trajectory-Constrained Deep Latent Visual Attention for Improved Local Planning in Presence of Heterogeneous Terrain

We present a reward-predictive, model-based learning method featuring trajectory-constrained visual attention for use in mapless, local visual navigation tasks. Our method learns to place visual attention at locations in latent image space which follow trajectories caused by vehicle control actions to later enhance predictive accuracy during planning. Our attention model is jointly optimized by the task-specific loss and additional trajectory-constraint loss, allowing adaptability yet encouraging a regularized structure for improved generalization and reliability. Importantly, visual attention is applied in latent feature map space instead of raw image space to promote efficient planning. We validated our model in visual navigation tasks of planning low turbulence, collision-free trajectories in off-road settings and hill climbing with locking differentials in the presence of slippery terrain. Experiments involved randomized procedural generated simulation and real-world environments. We found our method improved generalization and learning efficiency when compared to no-attention and self-attention alternatives.
Art Image Compression Based on Lossless LZW Hashing Ciphering Algorithm

Abstract Color image compression is a good way to encode digital images by decreasing the number of bits wanted to supply the image. The main objective is to reduce storage space, reduce transportation costs and maintain good quality. In current research work, a simple effective methodology is proposed for the purpose of compressing color art digital images and obtaining a low bit rate by compressing the matrix resulting from the scalar quantization process (reducing the number of bits from 24 to 8 bits) using displacement coding and then compressing the remainder using the Mabel ZF algorithm Welch LZW. The proposed methodology maintains the quality of the reconstructed image. Macroscopic and quantitative experimental results on technical color images show that the proposed methodology gives reconstructed images with a high PSNR value compared to standard image compression techniques.
The inverse of circulant matrices over GF(2 )

To search involutory of circulant matrices is an important issue in cryptographic applications. However, a circulant MDS matrix in AES can not be involutory. In this paper, we compute the inverse of circulant matrices of size 2n×2n whose entries are over the GF(2m) for n≥3. The computation starts with a computer program, which generates the cofactors of a circulant matrix of size 8×8. Based on the result, the inverse of a circulant matrix can be recursively constructed.
A Long-Short Term Memory Network for Chaotic Time Series Prediction

This paper demonstrates the development of an Long-Short Term Memory (LSTM) network and its application for predicting Chaotic Time Series (CTS). LSTM is a Deep Learning (DL) architecture and a type of Recurent Neural Network (RNN). Its ability to memorise past inputs make it extremely suitable for studying data sequences, such as CTS. To test the performance of a developed LSTM, several simulation studies were realized in a Matlab environment. The developed DL architecture was used to predict well-known standard CTSs, namely Mackey-Glass (MG), Rössler, and Lorenz. The obtained results were compared with Adaptive N euro Fuzzy Inference system (ANFIS) and Distributed Adaptive Neuro-Fuzzy Architecture (DANF A), previously developed by the authors. The comparison is made on the basis of Root Mean Squared Error (RMSE). It was found that the proposed LSTM structure is able to generalize the generated series and it has higher accuracy than the other two models.