Reviewerly/exHarmony huggingface dataset:
A Preliminary Analysis of Password Guessing Algorithm

Recently, password guessing algorithms have received increased attention in the field of password security. In this paper, we present a brief review of various existing typical password guessing algorithms from the aspects of hypothesis, identified information, and theoretical models. We employ multiple criteria to understand and evaluate the performance of these algorithms. By analyzing the experimental results, we summarize the characteristics of different password guessing algorithms. We have experimentally proved that when the guess number is the same, the two algorithms guess more passwords than one algorithm. Furthermore, we propose a hybrid password guessing algorithm-PaMLGuess. The algorithm has both strong interpretability and generalization ability and uses probability mapping to solve the problem that the magnitudes of the probabilities given by different password guessing algorithms vary widely. Our work aims to gain a deeper understanding of an attacker’s capabilities and provide an improvement direction for password strength meters(PSMs) to help system administrators prevent the use of weak passwords.
A Fast 2-D Convolution Technique for Deep Neural Networks

Deep neural networks have revolutionized the technology industry as hardware capable of implementing them has become widely available. They have been utilized in various applications such as image and video processing, self driving cars and speech processing. Two Dimensional (2-D) Convolutions are widely used in Deep Neural Networks. There are several techniques available to perform this operation. They can be implemented using methods such as sliding window, matrix multiplication, vector multiplication etc. In this paper, we introduce a new and improved (2-D) convolution method called Single Partial Product 2-D Convolution (SPP2D Convolution) that will help calculate 2-D convolution in a fast and expedient manner. We demonstrate that the new SPP2D convolution will prevent recalculation of partial weights and we present theoretical analysis of our technique compared to some other popular techniques. According to our analysis, our technique can reduce the clock cycles related to input reuse by at least 3 times in comparison with the technique adopted in the work done in and about 9 times than the standard sliding window approach.
Realistic Talking Face Synthesis With Geometry-Aware Feature Transformation

Recent studies have shown remarkable success in synthesizing realistic talking faces by exploiting generative adversarial networks. However, existing methods are mostly target specific that cannot generate images of previously unseen people, and they suffer from artifacts such as blurriness and mismatching of facial details. In this paper, we tackle these problems by proposing a target-agnostic framework. We introduce a geometry-aware feature transformation module to achieve shape transfer while preserving the appearance of the source face. To further improve image quality of synthesized results, we present a multi-scale spatially-consistent transfer unit to maintain spatial consistency between the encoder and decoder features. Experimental results show that our model is able to synthesize photo-realistic talking faces which are previously unseen, outperforming state-of-the-art methods both qualitatively and quantitatively.
Towards a Continuous Assessment of Stability in (Self-)Adaptation Behaviour

Self-adaptation and self-organisation (SASO) have been introduced to technical systems as a response to the ever increasing complexity and interconnectedness of component systems. The basic idea is to react to environmental dynamics and disturbances by re-configuring the productive behaviour and/or the relations to other systems. In this paper, we assume that too frequent and too strong adaptation behaviour is not desirable - in turn, we have to determine a trade-off between stability and optimal utility. This is assumed to result in a kind of self-awareness for the overall SASO system. Therefore, we describe a first concept to measure stability and analyse the behaviour in two example scenarios.
Voice-based interface for accessible soundscape composition

This paper presents an Internet of Audio Things ecosystem devised to support soundscape composition via vocal interactions. The ecosystem involves a commercial voice-based interface and the cloud-based repository of audio content Freesound.org. The user-system interactions are exclusively based on vocal input/outputs, and differ from the conventional methods for retrieval and sound editing which involve a browser and programs running on a desktop PC. The developed ecosystem targets sound designers interested in soundscape composition and in particular the visually-impaired ones, with the aim of making the soundscape composition practice more accessible. We report the results of a user study conducted with twelve participants. Overall, results show that the interface was found usable and was deemed easy to use and to learn. Participants reported to have enjoyed using the system and generally felt that it was effective in supporting their creativity during the process of composing a soundscape.
Towards Efficient On-Chip Learning using Equilibrium Propagation

With the growing research and application of deep learning, there are increasing demands in the ability to re-train or improve models with new data in the field. The popular backpropagation algorithm are very effective when training large models offline, however, it requires considerable computational resources. Equilibrium Propagation is an energy based learning algorithm for neural networks proposed as an alternative to the traditional back propagation algorithm. With the forward and backward phase using almost the same computation, the algorithm is an interesting candidate for implementing on-chip learning. As a first step towards building the hardware, we apply quantization on the algorithm to study the feasibility of a digital implementation. We then introduce a hardware oriented network pruning method to reduce the number of computations and the memory usage by a factor of 2.7. This paper lays the foundation for the implementation of Equilibrium Propagation on digital hardware, and an provides alternative angle to the problem of on-chip learning.
Ambidextrous Socio-Cultural Algorithms

Metaheuristics are a class of algorithms with some intelligence and self-learning capabilities to find solutions to difficult combinatorial problems. Although the promised solutions are not necessarily globally optimal, they are computationally economical. In general, these types of algorithms have been created by imitating intelligent processes and behaviors observed in nature, sociology, psychology and other disciplines. Metaheuristic-based search and optimization is currently widely used for decision making and problem solving in different contexts. The inspiration for metaheuristic algorithms are mainly based on nature’s behaviour or biological behaviour. Designing a good metaheurisitcs is making a proper trade-off between two forces: Exploration and exploitation. It is one of the most basic dilemmas that both individuals and organizations constantly are facing. But there is a little researched branch, which corresponds to the techniques based on the social behavior of people or communities, which are called Social-inspired. In this paper we explain and compare two socio-inspired metaheuristics solving a benchmark combinatorial problem.
Semantic Segmentation on Compressed Video using Block Motion Compensation and Guided Inpainting

This paper addresses the problem of fast semantic segmentation on compressed video. Unlike most prior works for video segmentation, which perform feature propagation based on optical flow estimates or sophisticated warping techniques, ours takes advantage of block motion vectors in the compressed bitstream to propagate the segmentation of a keyframe to subsequent non-keyframes. This approach, however, needs to respect the inter-frame prediction structure, which often suggests recursive, multi-step prediction with error propagation and accumulation in the temporal dimension. To tackle the issue, we refine the motion-compensated segmentation using inpainting. Our inpainting network incorporates guided non-local attention for long-range reference and pixel-adaptive convolution for ensuring the local coherence of the segmentation. A fusion step then follows to combine both the motion-compensated and inpainted segmentations. Experimental results show that our method outperforms the state-of-the-art baselines in terms of segmentation accuracy. Moreover, it introduces the least amount of network parameters and multiply-add operations for non-keyframe segmentation.
Type I Attack For Generative Models

Generative models are popular tools with a wide range of applications. Nevertheless, it is as vulnerable to adversarial samples as classifiers. The existing attack methods mainly focus on generating adversarial examples by adding imperceptible perturbations to input, which leads to wrong result. However, we focus on another aspect of attack, i.e., cheating models by significant changes. The former induces Type II error and the latter causes Type I error. In this paper, we propose Type I attack to generative models such as VAE and GAN. One example given in VAE is that we can change an original image significantly to a meaningless one but their reconstruction results are similar. To implement the Type I attack, we destroy the original one by increasing the distance in input space while keeping the output similar because different inputs may correspond to similar features for the property of deep neural network. Experimental results show that our attack method is effective to generate Type I adversarial examples for generative models on large-scale image datasets.
A Proposed Model based on Modern Requirements to Optimize Hostel Resources in Oman

There are nearly 18 colleges and universities in Muscat such as Middle East College, Oman Tourism College, Arab Open University, Sultan Qaboos University and many others. The students come from different cities / countries in Oman because of that the Hostel becomes one of the successful businesses running these days. The aim of this research is to study and propose a Modern system for the student's hostel. This system will be catering to the needs of owners, employees, students and parents, where they will be able to manage the activities of the hostel in more active way and fast. Currently most of them, they use manual system to store their data and all the activity which must be store and reuse are in papers this data or record can be lost, in this case the employees will not be able to search about the available rooms and details of the residents. In addition, there are many calculation processes they must be clear about it such as the fees where in the papers they did many mistakes. However, the process such as add new records, update records, delete records and searching for specific records need and spend a lot of time, so this system will help them to have real and fast result to improve the work in the hostel. Making reports and share them with parents are a part of their difficulties.
Unsupervised Clustering through Gaussian Mixture Variational AutoEncoder with Non-Reparameterized Variational Inference and Std Annealing

Clustering has long been an important research topic in machine learning, and is highly valuable in many application tasks. In recent years, many methods have achieved high clustering performance by applying deep generative models. In this paper, we point out that directly using q(z|y, x) instead of resorting to the mean-field approximation (as is adopted in previous works) in Gaussian Mixture Variational Auto-Encoder can benefit the unsupervised clustering task. We improve the performance of Gaussian Mixture VAE, by optimizing it with a Monte Carlo objective (including the q(z|y, x) term), with non-reparameterized Variational Inference for Monte Carlo Objectives (VIMCO) method. In addition, we propose std annealing to stabilize the training process and empirically show its effects on forming well-separated embeddings with different variational inference methods. Experimental results on five benchmark datasets show that our proposed algorithm NVISA outperforms several baseline algorithms as well as the previous clustering methods based on Gaussian Mixture VAE.
Polka

We propose a method for controlling a device that draws letters and illustrations on large, flat surfaces. Similarly to conventional inkjet printers, the device ejects a volume of water from its nozzle, with the water droplets then forming dots on surfaces such as soil, concrete, and sand. Both the direction of the nozzle and the water pressure can be controlled to enable the device to draw arbitrary two-dimensional patterns within a semicircular region with a radius of six meters. The device can draw letters and illustrations. We have investigated the pressure, the shape of the nozzle, and droplet size in order to avoid further division of the droplets into even smaller droplets while traversing the air. In this paper, we introduce the mechanism of this device and demonstrate how a user can take advantage of this new drawing tool.
Cornet: Composite-Regularized Neural Network For Convolutional Sparse Coding

Sparse recovery via composite regularization is an interesting approach proposed recently in the literature. One could design nonconvex regularizers through a convex combination of sparsity-promoting penalties with known proximal operators. We develop a new algorithm, namely, convolutional proximal-averaged thresholding algorithm (C-PATA) for composite-regularized convolutional sparse coding (CR-CSC) based on the recently proposed idea of proximal averaging. We develop an autoencoder structure based on the deep-unfolding of C-PATA iterations into neural network layers, which results in the composite-regularized neural network (CoRNet) architecture. The convolutional learned iterative soft-thresholding algorithm becomes a special case of CoRNet. We demonstrate the efficacy of CoRNet considering applications to image denoising and inpainting, and compare the performance with state-of-the-art techniques such as BM3D, convolutional LISTA, and fast and flexible convolutional sparse coding (FFCSC).
Introducing the Concept of Interaction Model for Interactive Dimensionality Reduction and Data Visualization

This letter formally introduces the concept of interaction model (IM), which has been used either directly or tangentially in previous works but never defined. Broadly speaking, an IM consists of the use of a mixture of dimensionality reduction (DR) techniques within an interactive data visualization framework. The rationale of creating an IM is the need for simultaneously harnessing the benefit of several DR approaches to reach a data representation being intelligible and/or fitted to any user’s criterion. As a remarkable advantage, an IM naturally provides a generalized framework for designing both interactive DR approaches as well as readily-to-use data visualization interfaces. In addition to a comprehensive overview on basics of data representation and dimensionality reduction, the main contribution of this manuscript is the elegant definition of the concept of IM in mathematical terms.
Fish Image Classification Using Deep Convolutional Neural Network

Scientific research on species composition and geographical distribution of marine organisms is of great significance to the research of marine resources and the protection of rare species of marine life. In these studies, divers or underwater robots are often used to collect biological images, which are then manually classified by relevant experts. Manual-based classification is not only time-consuming but also prone to misjudge. Deep learning algorithms have also been applied in this field, but the classification performance is poor in general, mainly due to the low image quality and the small number of collected images. In response to this challenging, a fish classification algorithm based on Inception-V3 is proposed in this paper. First, data augmentation is realized by scaling, inverting, and panning of original images. Then transfer learning method is applied to improve the prediction accuracy. Experimental results show that the proposed method can effectively improve the classification accuracy, reaching about 89% for fish species.
Feature Preserving Smoothing Provides Simple and Effective Data Augmentation for Medical Image Segmentation

CNNs represent the current state of the art for image classification, as well as for image segmentation. Recent work suggests that CNNs for image classification suffer from a bias towards texture, and that reducing it can increase the network’s accuracy. We hypothesize that CNNs for medical image segmentation might suffer from a similar bias. We propose to reduce it by augmenting the training data with feature preserving smoothing, which reduces noise and high-frequency textural features, while preserving semantically meaningful boundaries. Experiments on multiple medical image segmentation tasks confirm that, especially when limited training data is available or a domain shift is involved, feature preserving smoothing can indeed serve as a simple and effective augmentation technique.
Recognizing Scoring in Basketball Game from AER Sequence by Spiking Neural Networks

The automatic score detection and recognition in basketball game has important application potentials, for examples, basketball technique analysis and 24 second control in the game. Although existing studies have been conducted on broadcast videos, most of them usually learned a machine learning algorithm on long videos recorded by traditional cameras. Address Event Representation (AER) sensor provides a possibility to deal with the problem by a human sensing manner. It represents the visual information as a series of spike-based events and records event sequences. Compared to traditional videos, AER events can fully utilize their addresses and timestamp information, forming precise spatio-temporal features with significantly less storage cost. More importantly, it issues spikes which can be naturally processed by human-style spiking neural networks (SNNs). In this paper, we propose to recognize scoring in basketball game from AER sequences. A new model is designed to extract dynamic features and discriminate different event streams using SNN. To handle the imbalance problem between positive and negative samples, we use an imbalanced Tempotron algorithm in our SNN model. Meanwhile, an AER sequence dataset of basketball games is collected. The experimental results demonstrate that our method achieves better performance compared with existing models.
A Generic approach for Pronominal Anaphora and Zero Anaphora resolution in Arabic language

This paper deals with the resolution of pronominal anaphora and zero anaphora in Arabic language. While researchers have treated the two phenomena separately, we propose a generic approach for both of them. Our resolution system combines a Q-learning reinforcement method and Word Embedding models. The Q-learning method uses syntactic criteria as preference factors to select candidate antecedents. It reinforces the best combination criteria for evaluating candidate antecedents. The Word Embedding models provide semantic similarity measures that help to validate the best antecedent. Our approach is evaluated on different type of Arabic texts and the obtained precision can reach79.37%.
Light-Net: Lightweight Object Detector

Currently, object detectors based on CNN, such as RetinaNet, Faster-RCNN, CornerNet series, can achieve good performance, but have some common drawbacks, like large calculation cost, high model complexity and slow detection speed. In this paper, a new lightweight object detector is proposed, which adopted a density-based approach to merge the real boxes. To reduce calculation cost and improve detection speed, the tactic of multi-scale output is adopted to predict objects of different sizes with features of different scales. Furthermore, a new lightweight network model is proposed, which can show better performance in computation, FPS, and model complexity. Meanwhile, the separation of convolution is used to improve the basic convolution layer, which can achieve better results under the same number of filters. In the experiments, we verified the capability of our methods based on ablation experiment and model evaluation, which demonstrates the superiority of our method. Moreover, we have also conducted deep network and multichannel experiments on MS-COCO2014 datasets and achieved 20.9% mAP performance.
Mobile Terminal Implementation of Image Filtering and Edge Detection Based on OpenCV

In order to meet the requirement of wide application and popularization of real-time image processing methods based on computer vision, this paper introduces Gaussian filter to reduce noise on the basis of real-time detection of target edges, and greatly improves the accuracy of real-time edge detection by improving Sobel edge detection algorithm. OpenCV-based image processing mobile port is implemented, and machine vision technology is introduced into smartphones with Linux as the operating system. The experimental results after transplantation show that the system meets the real-time processing requirements and can achieve high-precision detection and stable operation.
From simulation to reality: a implementable self-organized collision avoidance algorithm for autonomous UAVs

We present an implementable self-organized collision avoidance algorithm for UAVs (Unmanned Aerial Vehicle). The algorithm generates roundabouts to avoid collisions. It also calculates a safe reaction distance to start the collision avoidance maneuver based on relative velocity, UAV acceleration, communication frequency, and GPS inaccuracy. We also present our experimental testbed for autonomous UAVs, in which we have designed, developed, and validated the collision avoidance algorithm. The developed testbed comprises a modular architecture that includes a simulation environment, a set of real autonomous UAVs, and communication hardware. This architecture allows us to improve realistic models in the simulation and to test the same implementation in simulation as well as in real UAVs. We validate our collision algorithm in simulation and experiment and analyze in which conditions both results are similar and in which ones differ.
An Artificial Intelligence Algorithm to Automate Situation Management for Operators of Unmanned Aerial Vehicles

Unmanned Aerial Vehicle (UAV) operators must maintain high levels of situation awareness on their area of operation. To achieve this, they use the a Command and Control (C2) map, which is shared among forces, and regularly overloaded with data that is irrelevant to their operational mission. Operators require distilled information at the right timing. Yet, the existing filtering mechanisms for C2 maps are layer-based and insufficient. We propose a new approach to automatically and dynamically filter information items on the map based on environmental and mission context. To achieve this, we introduce a three-tiers artificial intelligence (AI) based algorithm (GiCo-MAF), where we delineate the use of machine learning (ML) models to support UAV missions. For the GiCoMAF development, tagged data was collected in simulated experimental runs with professional UAS operators. Different types of ML models were evaluated and fitted into the algorithm. The models achieved a relatively high accuracy at modeling human preference and area of interest. The approach presented in this study can be further implemented to support time-critical spatial-temporal operational problems.