Reviewerly/exHarmony huggingface dataset:
Photorealistic Facial Wrinkles Removal

Editing and retouching facial attributes is a complex task that usually requires human artists to obtain photo-realistic results. Its applications are numerous and can be found in several contexts such as cosmetics or digital media retouching, to name a few. Recently, advancements in conditional generative modeling have shown astonishing results at modifying facial attributes in a realistic manner. However, current methods are still prone to artifacts, and focus on modifying global attributes like age and gender, or local mid-sized attributes like glasses or moustaches. In this work, we revisit a two-stage approach for retouching facial wrinkles and obtain results with unprecedented realism. First, a state of the art wrinkle segmentation network is used to detect the wrinkles within the facial region. Then, an inpainting module is used to remove the detected wrinkles, filling them in with a texture that is statistically consistent with the surrounding skin. To achieve this, we introduce a novel loss term that reuses the wrinkle segmentation network to penalize those regions that still contain wrinkles after the inpainting. We evaluate our method qualitatively and quantitatively, showing state of the art results for the task of wrinkle removal. Moreover, we introduce the first high-resolution dataset, named FFHQ-Wrinkles, to evaluate wrinkle detection methods.
TREAT: Automated Construction and Maintenance of Probabilistic Knowledge Bases from Logs (Extended Abstract)

Knowledge bases (KBs) are ideal vehicles for tackling many challenges, such as Query Answering, Root Cause Analysis. Given that the world is changing over time, previously acquired knowledge can become outdated. Thus, we need methods to update the knowledge when new information comes and repair any identified faults in the constructed KBs. However, to the best of our knowledge, there are few research works in this area. In this paper, we propose a system called TREAT (Tacit Relation Extraction and Transformation) to automatically construct a probabilistic KB which is continuously self-updating such that the knowledge remains consistent and up to date.
Spatial Group-Wise Enhance: Enhancing Semantic Feature Learning in CNN

The success of attention modules in CNN has attracted increasing and widespread attention over the past years. However, most existing attention modules fail to consider two important factors: (1) For images, different semantic entities are located in different areas, thus they should be associated with different spatial attention masks; (2) most existing framework exploits individual local or global information to guide the generation of attention masks, which ignores the joint information of local-global similarities that can be more effective. To explore these two ingredients, we propose the Spatial Group-wise Enhance (SGE) module. SGE explicitly distributes different but accurate spatial attention masks for various semantics, through the guidance of local-global similarities inside each individual semantic feature group. Furthermore, SGE is lightweight with almost no extra parameters and calculations. Despite being trained with only category supervisions, SGE is effective in highlighting multiple active areas with various high-level semantics (such as the dog’s eyes, nose, etc.). When integrated with popular CNN backbones, SGE can significantly boost their performance on image recognition tasks. Specifically, based on ResNet101 backbones, SGE improves the baseline by 0.7% Top-1 accuracy on ImageNet classification and 1.6 $$\sim $$ 1.8% AP on COCO detection tasks. The code and pretrained models are available at https://github.com/implus/PytorchInsight.
Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects

In this work we present two video test data sets for the novel computer vision (CV) task of out of distribution tracking (OOD tracking). Here, OOD objects are understood as objects with a semantic class outside the semantic space of an underlying image segmentation algorithm, or an instance within the semantic space which however looks decisively different from the instances contained in the training data. OOD objects occurring on video sequences should be detected on single frames as early as possible and tracked over their time of appearance as long as possible. During the time of appearance, they should be segmented as precisely as possible. We present the SOS data set containing 20 video sequences of street scenes and more than 1000 labeled frames with up to two OOD objects. We furthermore publish the synthetic CARLA-WildLife data set that consists of 26 video sequences containing up to four OOD objects on a single frame. We propose metrics to measure the success of OOD tracking and develop a baseline algorithm that efficiently tracks the OOD objects. As an application that benefits from OOD tracking, we retrieve OOD sequences from unlabeled videos of street scenes containing OOD objects.
On a stronger notion of connectedness in c-spaces

In this article, a stronger form of connectedness called Y-connectedness in c-spaces is introduced and some of its properties are studied. Using the notion of touching, some conditions under which union of Y-connected sub c-spaces of a c-space become Y-connected is also discussed.
(Re)Turning to Hypertext: Mattering Digital Learning Spaces

This essay argues for a (re)turn to the potential of hypertext by entangling it with/in material rhetorics. A (re)turning—turning over again—troubles and decolonizes traditional understandings of hypertext as either technological product or trope by demonstrating how hypertextuality is [also] a matter of matter. More specifically, this essay uses ethnography as “deep theorization” to extend Angela Haas’s notion of wampum-as-hypertext. I analyze the hypertextual rhetoricity of matter in students’ digital learning environments and demonstrate how these places iteratively become agential and transformative, thus (re)making the digital learning experience. This theorization of digital-learning-spaces-as-hypertexts draws attention to the need to (re)conceptualize digital spaces in terms beyond that of efficiency and carefully (re)consider what it means to [better] teach with/in digitally mediated environments.
A generalization of the pascal matrix and an application to coding theory

AbstractSuppose that m,k,t are integers with m,k≥ 1 and 0≤t and A is the k×k matrix with the (i,j)-entry (t+(j−1)mi−1). When t = 0 and m = 1, this is the upper triangular Pascal matrix. Here, first we study the properties of this matrix, in particular, we find its determinant and its LDU decomposition and also study its inverse. Then by using this matrix we present a generalization of the Mattson-Solomon transform and a polynomial formulation for its inverse to the case that the sequence length and the characteristic of the base field are not coprime. At the end, we use this generalized Mattson-Solomon transform to present a lower bound on the length of repeated root cyclic codes, which can be seen as a generalization of the BCH bound.Keywords: Upper triangular pascal matrixmattson-solomon polynomialgeneralized discrete fourier transformhasse derivativerepeated-root cyclic codes2020 Mathematical Subject Classifications: 15B3615A1505A1015B3311T71 AcknowledgmentsThe authors would like to thank the reviewers for their nice comments which improved the presentation of the paper.Disclosure statementNo potential conflict of interest was reported by the author(s).
Reaching for the “Cloud”

Cloud computing adoption and utilization is gaining prominence in most developing countries. Its adoption is influenced by several factors, which can constitute a limitation rather than an advantage for firms. This research, therefore, explored the issues surrounding the adoption of cloud computing by small and medium-sized enterprises (SMEs) in a developing economy. An SME operating in Ghana for over five years was purposively selected as the case for the study. Technology-organization-environment (TOE) framework served as a guiding lens. Interviews were held with selected staff of the case firm. Data were analyzed using the Miles and Huberman's transcendental realism technique. It was discovered that the motivation for cloud computing adoption is the possibility of getting a state-of-the-art IT infrastructure at the lowest cost possible. The study presents the factors that influenced cloud computing adoption in the SME. The study contributes to improving the present understanding of cloud computing as an SME's strategic tool for operating within a developing economy.
Neural blind deconvolution with Poisson data

Abstract Blind Deconvolution problem is a challenging task in several scientific imaging domains, such as Microscopy, Medicine and Astronomy. The Point Spread Function inducing the blur effect on the acquired image can be solely approximately known, or just a mathematical model may be available. Blind deconvolution aims to reconstruct the image when only the recorded data is available. In the last years, among the standard variational approaches, Deep Learning techniques have gained interest thanks to their impressive performances. The Deep Image Prior framework has been employed for solving this task, giving rise to the so-called neural blind deconvolution (NBD), where the unknown blur and image are estimated via two different neural networks. In this paper, we consider microscopy images, where the predominant noise is of Poisson type, hence signal-dependent: this leads to consider the generalized Kullback–Leibler as loss function and to couple it with regularization terms on both the blur operator and on the image. Furthermore, we propose to modify the standard NBD formulation problem, by including for the blur kernel an upper bound which depends on the optical instrument. A numerical solution is obtained by an alternating Proximal Gradient Descent-Ascent procedure, which results in the Double Deep Image Prior for Poisson noise algorithm. We evaluate the proposed strategy on both synthetic and real-world images, achieving promising results and proving that the correct choice of the loss and regularization functions strongly depends on the application at hand.
Comparative approach to different convolutional neural network (CNN) architectures applied to human behavior detection

Medical diagnostics, product classification, surveillance and detection of inappropriate behavior are becoming increasingly sophisticated due to the development of methods based on image analysis using neural networks. Considering this, in this work, we evaluate state-of-the-art convolutional neural network architectures proposed in recent years to classify the driving behavior and distractions of drivers. Our main goal is to measure the performance of such architectures using only free resources (i.e., free graphic processing unit, open source) and to evaluate how much of this technological evolution is available to regular users.
Video Captioning by Learning from Global Sentence and Looking Ahead

Video captioning aims to automatically generate natural language sentences describing the content of a video. Although encoder-decoder-based models have achieved promising progress, it is still very challenging to effectively model the linguistic behavior of humans in generating video captions. In this paper, we propose a novel video captioning model by learning from gLobal sEntence and looking AheaD, LEAD for short. Specifically, LEAD consists of two modules: a Vision Module (VM) and a Language Module (LM) . Thereinto, VM is a novel attention network, which can map visual features to high-level language space and model entire sentences explicitly. LM can not only effectively make use of the information of the previous sequence when generating the current word, but also have a look at the future word. Therefore, based on VM and LM, LEAD can obtain global sentence information and future word information to make video captioning more like a fill-in-the-blank task than a word-by-word sentence generation. In addition, we also propose an autonomous strategy and a multi-stage training scheme to optimize the model, which can mitigate the problem of information leakage. Extensive experiments show that LEAD outperforms some state-of-the-art methods on MSR-VTT, MSVD, and VATEX, demonstrating the effectiveness of the proposed approach in video captioning. In addition, we release the code of our proposed model to be publicly available. 1
Design of intelligent module design for humanoid translation robot by combining the deep learning with blockchain technology

To accelerate the deep application of deep learning in text data processing, an English statistical translation system is established and applied to the question answering of humanoid robot. Firstly, the model of machine translation based on recursive neural network is implemented. A crawler system is established to collect English movie subtitle data. On this basis, an English subtitle translation system is designed. Then, combined with sentence embedding technology, the Particle Swarm Optimization (PSO) algorithm of meta-heuristic algorithm is adopted to locate the defects of translation software. A translation robot automatic question and answer interactive module is constructed. Additionally, the hybrid recommendation mechanism based on personalized learning is built using blockchain technology. Finally, the performance of translation model and software defect location model is evaluated. The results show that the Recurrent Neural Network (RNN) embedding algorithm has certain effect of word clustering. RNN embedded model has a strong ability to process short sentences. The strongest translated sentences are between 11 and 39 words long, while the weakest translated sentences are between 71 and 79 words long. Therefore, the model must strengthen the processing of long sentences, especially character-level input. The average sentence length is much longer than word-level input. The model based on PSO algorithm shows good accuracy in different data sets. This model averages better performance on Tomcat, standard widget toolkits, and Java development tool datasets than other comparison methods. The average reciprocal rank and average accuracy of the weight combination of PSO algorithm are very high. Moreover, this method is greatly affected by the dimension of the word embedding model, and the 300-dimension word embedding model has the best effect. To sum up, this study proposes a good statistical translation model for humanoid robot English translation, which lays the foundation for intelligent interaction between humanoid robots.
Privacy preserving via multi-key homomorphic encryption in cloud computing

As the world grapples with the COVID-19 and its variants, multi-user collaboration by means of cloud computing is ubiquitous. How to make better use of cloud resources while preventing user privacy leakage has become particularly important. Multi-key homomorphic encryption(MKHE) can effectively deal with the privacy disclosure issue during the multi-user collaboration in the cloud computing setting. Firstly, we improve the DGHV homomorphic scheme by modifying the selection of key and the coefficients in encryption, so as to eliminate the restriction on the parity of the ciphertext modulus in the public key. On this basis, we further propose a DGHV-type MKHE scheme based on the number theory. In our scheme, an extended key is introduced for ciphertext extension, and we prove that it is efficient in performance analysis. The semantic security of our schemes is proved under the assumption of error-free approximate greatest common divisor and the difficulty of large integer factorization. Furthermore, the simulation experiments show the availability and computational efficiency of our MKHE scheme. Therefore, our scheme is suitable for the multi-user scenario in cloud environment.
An intelligent film recommender system based on emotional analysis

The existing personalized film recommendation methods take the user's historical rating as an important basis for recommendation. However, the user's rating standards are different, so it is difficult to mine the user's real preferences and form accurate push. Therefore, to achieve high-quality personalized recommendation of films, it is particularly important to mine the emotion of user reviews. In this article, a personalized recommendation method based on sentiment analysis of film reviews is proposed, where natural language processing technology is used to mine the emotional tendency of user reviews. The multi-modal emotional features are weighted and the weighted fusion feature vector after PSO is taken as the overall emotion vector, then the emotional similarity of weighted fusion is calculated by considering the time factor of content publishing and the average emotional tendency of users. By calculating the matching degree of emotional value between users and films, the top-N film recommendation for target users is given. The test results show that the effect of the personalized film recommendation system based on multimodality is superior to that of the comparison method, which effectively solves the problem of different user rating scales, and really increases users' interest in watching films.
Lightweight Tennis Ball Detection Algorithm Based on Robomaster EP

To address the problems of poor recognition effect, low detection accuracy, many model parameters and computation, complex network structure, and unfavorable portability to embedded devices in traditional tennis ball detection algorithms, this study proposes a lightweight tennis ball detection algorithm, YOLOv5s-Z, based on the YOLOv5s algorithm and Robomater EP. The main work is as follows: firstly, the lightweight network G-Backbone and G-Neck network layers are constructed to reduce the number of parameters and computation of the network structure. Secondly, convolutional coordinate attention is incorporated into the G-Backbone to embed location information into channel attention, which enables the network to obtain location information of a larger area through multiple convolutions and enhances the expression ability of mobile network learning features. In addition, the Concat module in the original feature fusion is modified into a weighted bi-directional feature pyramid W-BiFPN with settable learning weights to improve the feature fusion capability and achieve efficient weighted feature fusion and bi-directional cross-scale connectivity. Finally, the Loss function EIOU Loss is introduced to split the influence factor of the aspect ratio and calculate the length and width of the target frame and anchor frame, respectively, combined with Focal-EIOU Loss to solve the problem of imbalance between complex and easy samples. Meta-ACON’s activation function is introduced to achieve an adaptive selection of whether to activate neurons and improve the detection accuracy. The experimental results show that compared with the YOLOv5s algorithm, the YOLOv5s-Z algorithm reduces the number of parameters and computation by 42% and 44%, respectively, reduces the model size by 39%, and improves the mean accuracy by 2%, verifying the effectiveness of the improved algorithm and the lightweight of the model, adapting to Robomaster EP, and meeting the deployment requirements of embedded devices for the detection and identification of tennis balls.
Evaluation and Improvement of Internet Printing Protocol Based on HCPN Model Detection Method

The Internet Printing Protocol (IPP) is a bridge between hosts and printers, and is supported by more than 98 percent of printers today. In addition to supporting local use, the IPP protocol also supports online use. Although this can expand the scope of its application, it has also introduced potential risks to user data. IPP has security components that should be able to guarantee confidentiality, integrity, and non-repudiation. In order to verify whether its security components can achieve this goal, this study modeled the 0-RTT authentication process of the IPP protocol based on Petri net theory and CPN Tools, introducing the improved Dolev–Yao adversary model to perform security evaluation on the protocol model. The result showed that the server could not resist the adversary’s replay attacks on early data. Accordingly, we proposed an improved authentication scheme that introduced a random number signature to enhance the server’s anti-replay capability. Using the same attack model to verify, the result proved that the new scheme was feasible and effective. The method used in this article could easily observe the movement of the security protocol message flow and the specific actions of each participant (including the adversary), which ensured researchers could easily locate the protocol defects and make improvements.
A Trajectory Generation Method for Multi-Rotor UAV Based on Adaptive Adjustment Strategy

In order to fly safely and autonomously in complex environments, UAVs need to be able to plan their trajectories in real-time. This paper proposes an improved B-spline-based trajectory generation method that can generate safe, smooth, and kinodynamically feasible trajectories in real-time. This paper firstly introduces the principle of error upper bound of the B-spline curve and proposes a new trajectory safety assurance method; then, the loss function of trajectory is constructed based on safety, smoothness, and flight time; finally, a parameter adaptive trajectory optimization method is proposed, so that obtain the safe trajectory. Compared with the existing methods, the proposed method has two important improvements: (1) it solves the problem of overly conservative safety distance estimation at control points, improves the trajectory smoothness, and reduces the required flight time; (2) it proposes a trajectory optimization method with adaptive adjustment of safety distance parameters, which improves the quality and success rate of the planned trajectory. We validate our proposed method in simulation and real-world tasks, and the test results show that the method proposed in this paper can significantly improve the quality of the generated trajectory.
Development of a Worker-Following Robot System: Worker Position Estimation and Motion Control under Measurement Uncertainty

This study proposes a sensor data process and motion control method for a mobile platform essential for transporting finished products or subsidiary materials in a smart factory. We developed a system that recognizes a fiducial marker printed on the work clothes worn by a worker, estimates the worker’s location, and follows the worker using the estimated location. To overcome the limitations of simulation-based research, gait data on a two-dimensional plane were derived through a human gait model and an error model according to the distance between the image sensor and the reference marker. The derived gait data were defined as the localization result for the worker, and a Kalman filter was used to robustly address the uncertainty of the localization result. A virtual spring-damper system was applied to follow the Mecanum wheel-based mobile platform workers. The performance of the proposed algorithm was demonstrated through comparative simulations with existing methods.
Task Assignment for UAV Swarm Saturation Attack: A Deep Reinforcement Learning Approach

Task assignment is a challenging problem in multiple unmanned aerial vehicle (UAV) missions. In this paper, we focus on the task assignment problem for a UAV swarm saturation attack, in which a deep reinforcement learning (DRL) framework is developed. Specifically, we first construct a mathematical model to formulate the task assignment problem for a UAV swarm saturation attack and consider it as a Markov Decision Process (MDP). We then design a policy neural network using the attention mechanism. We also propose a training algorithm based on the policy gradient method so that our agent can learn an effective task assignment policy. The experimental results have shown that our DRL method can generate high-quality solutions for different problem scales, which meets the requirements of real-time and flexibility in the actual situation.