Reviewerly/exHarmony huggingface dataset:
A Chatbot Solution for Self-Reading Energy Consumption via Chatting Applications

To mitigate financial loss and follow the recommended sanitary measures due to the COVID-19 pandemic, self-reading, a method in which a consumer reads and reports his own energy consumption, has been presented as an efficient alternative for power companies. In such context, this work presents a solution for self-reading via chatbot in chatting applications. This solution is under development as part of a research and development (R&D) project. It is integrated with a method based on image processing that automatically reads the energy consumption and recognizes the identification code of a meter for validation purposes. Furthermore, all processes utilize cognitive services from the IBM Watson platform to recognize intentions in the dialog with the consumers. The dataset used to validate the proposed method for self-reading contains examples of analogical and digital meters used by Equatorial Energy group. Preliminary results presented accuracies of 77.20% and 84.30%, respectively, for the recognition of complete reading sequences and identification codes in digital meters and accuracies of 89% and 95.20% in the context of analogical meters. Considering both meter types, the method obtains an accuracy per digit of 97%. The proposed method was also evaluated with UFPR-AMR public dataset and achieves a result comparable to the state of the art.
Particle System-Based Multi-Hierarchy Dynamic Visualization of Ocean Current Data

In three-dimensional (3D) digital Earth environment, there are many problems when using the existing methods to express the ocean current, such as uneven distribution of seed points, density leap in scale change and messy visualization. In this paper, a new dynamic visualization method of multi-hierarchy flow field based on particle system is proposed; Specifically, three typical spherical uniform algorithms are studied and compared, and the streamline becoming denser from the equator to the poles on globe is eliminated by placing seed points using Marsaglia polar method as the most efficient. In addition, a viewport-adaptive adjustment algorithm is proposed, which realizes that the density of particles is always suitable to any viewing distance during continuous zooming. To solve the visual representation deficiency, we design a new dynamic pattern to enhance the expression and perception of current, which makes up for the shortcoming of the arrow glyph and streamline methods. Finally, a prototype of GPU parallel and viewport coherence is achieved, whose feasibility and effectiveness are verified by a series of experiments. The results show that our method can not only represent ocean current data clearly and efficiently, but also has outstanding uniformity and hierarchy effect.
CapsNet meets ORB: A deformation‐tolerant baseline for recognizing distorted targets

Pattern recognition from two-dimensional (2D) images is an indispensable tache in computer vision. However, one legacy hinders its progress: multifarious visual distortions (e.g., partially occluded signs, fisheye respective, affine or 3D projections) caused by spatiotemporal-varying perturbations (e.g., shrunk, sharpening, overexposure, jitter, and motion) significantly degrade the performance of neural networks in terms of high-level intelligent behaviors (e.g., target localization and recognition). Leveraging the growing availability of capsule network (CapsNet), we suppress the deformation effect to the final prediction by proposing a CapsNetORB framework to implement distorted target recognition. Two highlights, the customized Siamese CapsNet (S-CapsNet) and vector-based oriented fast and rotated brief (VB-ORB), can cast a mutual positive stimulation: the former encodes capsule feature vectors for the later, whilst the later detects space-scale invariant interval dimensions (instead of pixels) to bridge association between source standard images (high-quality training images) and distorted ones (testing images). Thus, the category of one source standard image owning the most correspondences is the final predicted category. We believe that capsule vectors own higher representability and stability compared with conventional pixels/feature maps, which can be well exploited in feature learning while resisting visual distortions. Experimentally, we show that employing our pipeline for distorted target categorization can outperform state-of-the-arts by delivering promising performance on CUB-200-2011, Stanford Dogs, Stanford Cars, and our hand-crafted data set.
Development of Head Motion Controlled Wheelchair

Mobility assistance is vital for physically challenged people and elderly people. The objectives of this paper are to design a wheelchair that can be controlled by head motions. Quadriplegics who can't use their hands or legs but can move their heads could benefit from this system. The disabled individual may maneuver this wheelchair in any direction using head tilt motions. The construction of a head motion-controlled wheelchair has been made possible by the use of tilt sensors and diode logic. The prototype of the system is built, and its operation is found to be satisfactory. In comparison to traditional joystick-controlled wheelchairs, the proposed wheelchair is developed at a lesser cost. The head motion-controlled wheelchair is designed to assist quadriplegic patients & elderly people as they can operate it without any external assistance.
An Intelligent framework for E-Recruitment System Based on Text Categorization and Semantic Analysis

In the field of online job recruiting, accurate job and resume categorization is critical for both the seeker and the recruiter. Using Natural Language Processing (NLP) technology we have developed an autonomous text classification system that POS tag, tokenizes, Lemmatize the data. We have utilized Phrase Matcher to calculate the score of resumes based on recruiter's information, suggest lacking skills to users, and provide the top resumes to the recruiter. Finally, the proposed system is presented together with its findings and analysis. We divided candidates into groups based on the information in their resumes. We used domain adaptation due to the sensitive nature of the resumes content. A Word Order Similarity between Sentences is used to categorize the resume data on large dataset of job description. The System is evaluated and resulted in improved precision and recall.
Grid management system through functionalitites and potentialities of an Energy Blockchain

Propose innovation through new technologies is a challenge when the aimed system is well-structured with the actual architecture. That said, this work assigns the functionalities and potentialities of an Energy Blockchain technology with the needed local regulations, management, and assessment of an energy grid, towards a decentralized distributed network. It is presented using programming languages JavaScript and Solidity how a simple Blockchain can be structured and how several functions can automatize even more the energy niche. Considering the state-of-art of this technology it would contribute with gains in transparency, security, cost reduction and efficiency to our actual distribution energy grid. Therefore, a walkthrough to reach a parity between the actual regulation concepts and programmed smart contracts, in terms of a public Blockchain platform, is presented aspiring gains to systematic users attained in the optimization of the prosumers state, energy matrix configuration, price, data acquisition., and responsiveness of law resolutions.
Structure-transformed Texture-enhanced Network for Person Image Synthesis

Pose-guided virtual try-on task aims to modify the fashion item based on pose transfer task. These two tasks that belong to person image synthesis have strong correlations and similarities. However, existing methods treat them as two individual tasks and do not explore correlations between them. Moreover, these two tasks are challenging due to large misalignment and occlusions, thus most of these methods are prone to generate unclear human body structure and blurry fine-grained textures. In this paper, we devise a structure-transformed texture-enhanced network to generate high-quality person images and construct the relationships between two tasks. It consists of two modules: structure-transformed renderer and texture-enhanced stylizer. The structure-transformed renderer is introduced to transform the source person structure to the target one, while the texture-enhanced stylizer is served to enhance detailed textures and controllably inject the fashion style founded on the structural transformation. With the two modules, our model can generate photorealistic person images in diverse poses and even with various fashion styles. Extensive experiments demonstrate that our approach achieves state-of-the-art results on two tasks.
A method for eliminating the disturbance of pseudo-textural-direction in ultrasound image feature extraction

Ultrasound imaging is widely used in clinical diagnosis. However, due to the mechanism of B-mode sector scanning in ultrasound imaging, there exists pseudo-textural-direction (PTD) that is perpendicular to the ultrasonic beam and varies with the change of the beam scanning angle. The existence of PTD reduces the resolution of the image, and brings disturbances to subsequent tasks such as feature extraction, feature screening and segmentation. A method proposed to eliminate the disturbance of PTD consists of three stages. Firstly, a Fourier transform is performed on every superpixel that is a small homogeneous region generated by over-segmentation to obtain a spectrum of the superpixel. Then, ellipse fitting is conducted on the spectrum to detect the deflection angle of textural direction in the superpixel. Finally, the textural direction of the superpixel is corrected by the obtained deflection angle and the co-occurrence matrix texture features are extracted. The effectiveness of the proposed method was verified by segmentation experiments of ultrasound guided images of uterine fibroid acquired from high intensity focused ultrasound (HIFU) therapy based on a split and merge framework. The experimental results were evaluated by quantitative metrics that include true positive (TP), false positive (FP), similarity (SI), Hausdorff distance (HD) and mean absolute distance (MAD). All the metrics were improved in comparison with conventional algorithms when the features screening and segmentation algorithms worked with the proposed feature extraction algorithm. Therefore, the existence of PTD is demonstrated and the proposed method to eliminate its disturbance is feasible.
A Separation of $$\gamma $$ and b via Thue–Morse Words

We prove that for \(n\ge 2\), the size \(b(t_n)\) of the smallest bidirectional scheme for the nth Thue–Morse word \(t_n\) is \(n+2\). Since Kutsukake et al. [SPIRE 2020] show that the size \(\gamma (t_n)\) of the smallest string attractor for \(t_n\) is 4 for \(n \ge 4\), this shows for the first time that there is a separation between the size of the smallest string attractor \(\gamma \) and the size of the smallest bidirectional scheme b, i.e., there exist string families such that \(\gamma = o(b)\).
Suspicious Person Retrieval from UAV-sensors based on part level deep features

Intelligent video surveillance systems represent a potent tool for preserving human security in public places. Indeed, these surveillance systems are requested in several real-life scenarios, in order to assist security guards by alerting them in abnormal situations and helping them to retrieve a suspicious person. Especially, intelligent video surveillance systems based on UAV-sensors have the asset of monitoring large as well as difficult access spaces. In this scope, we introduce a new approach for suspicious person retrieval from UAV-sensors. The proposed approach implies two complementary phases which are an offline phase and an inference phase. Within these phases, a scene stabilization step is carried out. The offline phase allows building the non-person/person model as well as the person retrieval model. Nonetheless, the inference phase enables to detect persons and retrieve suspicious ones using the already generated models. The main contribution of the proposed approach is the use of part-level deep features in order to retrieve persons. The experimental results validate the contributions of our approach compared to the state-of-the-art approaches.
webFuzz: Grey-Box Fuzzing for Web Applications

Fuzzing is significantly evolved in analysing native code, but web applications, invariably, have received limited attention until now. This paper designs, implements and evaluates webFuzz, a gray-box fuzzing prototype for discovering vulnerabilities in web applications.
Explainable cyber-physical energy systems based on knowledge graph

Explainability can help cyber-physical systems alleviating risk in automating decisions that are affecting our life. Building an explainable cyber-physical system requires deriving explanations from system events and causality between the system elements. Cyber-physical energy systems such as smart grids involve cyber and physical aspects of energy systems and other elements, namely social and economic. Moreover, a smart-grid scale can range from a small village to a large region across countries. Therefore, integrating these varieties of data and knowledge is a fundamental challenge to build an explainable cyber-physical energy system. This paper aims to use knowledge graph based framework to solve this challenge. The framework consists of an ontology to model and link data from various sources and graph-based algorithm to derive explanations from the events. A simulated demand response scenario covering the above aspects further demonstrates the applicability of this framework.
Hierarchical learning from human preferences and curiosity

Abstract Recent success in scaling deep reinforcement algorithms (DRL) to complex problems has been driven by well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally extremely sparse. One solution to this problem is to introduce human guidance to drive the agent’s learning. Although low-level demonstrations is a promising approach, it was shown that such guidance may be difficult for experts to demonstrate since some tasks require a large amount of high-quality demonstrations. In this work, we explore human guidance in the form of high-level preferences between sub-goals, leading to drastic reductions in both human effort and cost of exploration. We design a novel hierarchical reinforcement learning method that introduces non-expert human preferences at the high-level, and curiosity to drastically speed up the convergence of subpolicies to reach any sub-goals. We further propose a strategy based on curiosity to automatically discover sub-goals. We evaluate the proposed method on 2D navigation tasks, robotic control tasks, and image-based video games (Atari 2600), which have high-dimensional observations, sparse rewards, and complex state dynamics. The experimental results show that the proposed method can learn significantly faster than traditional hierarchical RL methods and drastically reduces the amount of human effort required over standard imitation learning approaches.
Incremental human action recognition with dual memory

Incremental learning is a topic of great interest in the current state of machine learning research. Real-world problems often require a classifier to incorporate new knowledge while preserving what was learned before. One of the most challenging problems in computer vision is Human Action Recognition (HAR) in videos. However, most of the existing works approach HAR from a non-incremental point of view. This work proposes a framework for performing HAR in the incremental learning scenario called Incremental Human Action Recognition with Dual Memory (IHAR-DM). IHAR-DM contains three main components: a 3D convolutional neural network for capturing Spatio-temporal features; a Triplet Network to perform metric learning; and the dual-memory Extreme Value Machine, which is introduced in this work. The proposed method is compared with 10 other state-of-the-art incremental learning models. We propose five experimental settings containing different numbers of tasks and classes using two widely known HAR datasets: UCF-101 and HMDB51. Our results show superior performance in terms of Normalized Mutual Information (NMI) and Inter-task Intransigence (ITI), which is a new metric proposed in this work. Overall results show the feasibility of the proposal for real HAR problems, which mostly present the requirements imposed by incremental learning.
Study on the Relationship Between Employee Satisfaction and Corporate Performance in Japan via Text Mining

For the past 30 years, under the stagnant Japanese economy, companies have adopted various management strategies, and employees’ sense of values has become more diverse. In this study, we conduct research on the relationship between employee satisfaction and corporate performance. Although survey-based methods are used as a common approach to assessing employee satisfaction, we determined employee satisfaction by performing sentiment analysis based on online reviews of employees. In addition, we developed two performance indicators based on financial cash flow figures, which include the growth rate of operating cash flow and the ratio of investing cash flow to operating cash flow. These figures demonstrate corporate operating cash flow performance, as well as management’s attitude toward investment. By analyzing Japanese companies in the information and communication, electric appliance, and transportation equipment industries, we obtained three findings: 1) the companies with high operating cash flow growth affects or are positively affected by employee satisfaction with “Accomplishment” and “Promotion,” 2) the companies with low operating cash flow growth and aggressive investment over operating cash flow affect or are affected by variations in employee satisfaction with “Accomplishment,” 3) the companies in information and communication industry with high operating cash flow growth and aggressive investment over operating cash flow negatively affect or are affected by employee satisfaction with “corporate atmosphere.”
Virtual Keyboard Using Image Processing and Computer Vision

Today, with this fast-changing world, portability is one of the critical factors for consumers when choosing a personal computer (PC). Still, the conventional hardware keyboard is a significant limitation in desktops and notebooks. Virtual keyboard prototype solves hardware limitations of the keyboard by presenting an easy and economical alternative using minimum external hardware. In this paper, we have tried to design a virtual keyboard model, which is based on image processing using an inbuilt camera. After that, the basic framework of this can be modified to serve various types of augmented reality (AR), paper, and air keyboard purposes.
Towards Robust Dual-View Transformation via Densifying Sparse Supervision for Mammography Lesion Matching

A holistic understanding of dual-view transformation (DVT) is an enabling technique for computer-aided diagnosis (CAD) of breast lesion in mammogram, e.g., micro-calcification (\(\mu \)C) or mass matching, dual-view feature extraction etc. Learning a complete DVT usually relies on a dense supervision which indicates a corresponding tissue in one view for each tissue in another. Since such dense supervision is infeasible to obtain in practical, a sparse supervision of some traceable lesion tissues across two views is thus an alternative but will lead to a defective DVT, limiting the performance of existing CAD systems dramatically. To address this problem, our solution is simple but very effective, i.e., densifying the existing sparse supervision by synthesizing lesions across two views. Specifically, a Gaussian model is first employed for capturing the spatial relationship of real lesions across two views, guiding a following proposed LT-GAN where to synthesize fake lesions. The proposed novel LT-GAN can not only synthesize visually realistic lesions, but also guarantee appearance consistency across views. At last, a denser supervision can be composed based on both real and synthetic lesions, enabling a robust DVT learning. Experimental results show that a DVT can be learned via our densified supervision, and thus result in a superior performance of cross-view \(\mu \)C matching on INbreast and CBIS-DDSM dataset to the state-of-the-art methods.
R-SLAM: Optimizing Eye Tracking from Rolling Shutter Video of the Retina

We present a method for optimization-based recovery of eye motion from rolling shutter video of the retina. Our approach formulates eye tracking as an optimization problem that jointly estimates the retina’s motion and appearance using convex optimization and a constrained version of gradient descent. By incorporating the rolling shutter imaging model into the formulation of our joint optimization, we achieve state-of-the-art accuracy both offline and in real-time. We apply our method to retina video captured with an adaptive optics scanning laser ophthalmoscope (AOSLO), demonstrating eye tracking at 1 kHz with accuracies below one arcminute—over an order of magnitude higher than conventional eye tracking systems.
Data gap decomposed by auxiliary modality for NIR‐VIS heterogeneous face recognition

In the dark scene at night, the face images captured by ordinary visible light (VIS) are generally poor quality and very dim, while the near-infrared (NIR) can capture high definition and recognizable face images at night. The NIR-VIS Heterogeneous face recognition has become a hot research field, which helps to build an all-weather face recognition system. NIR-VIS HFR is sophisticated because of the large visual difference between NIR images and VIS images. In order to reduce the difficulty of such cross-modality invariant feature learning, this paper proposes a cross-modality data gap decomposed by auxiliary modality method (DGD) for NIR-VIS HFR. First, the brightness component (Y component) of VIS image YCbCr space is used as the auxiliary modality to decompose the cross-modality data gap. The lightness component retained the structural information of VIS image and was similar to the colour information of NIR modality; in this way, the huge gap between the NIR data and the VIS data is decomposed into two smaller gaps, thus reducing the difficulty of network learning. Second, the data of the three modalities are input into the weight sharing network and training under the combined guidance of cross-modality gap decomposition loss and intra-modality gap loss; in this way, the modality invariant features can be learned faster and better. Extensive experiments were conducted on two commonly used datasets CASIA NIR-VIS 2.0 and Oulu-CASIA NIR-VIS to evaluate DGD method. Experimental results indicate DGD method has competitive performance compared with the latest methods.