Reviewerly/exHarmony huggingface dataset:
Background Features-Based Novel Visual Ego-Motion Estimation

For localization in mobile robotics, usage of camera provides hundreds and thousands of information about its environment and poses a great challenge computationally in real-time applications. Furthermore, when it is applied in computer vision algorithms, its robustness turns out to be a vital factor. This paper contributes a robust and efficient visual Ego-motion estimation technique using a stream of images captured solely in a stereo camera. In many cases, nuisances caused due to instantly varying illumination, occlusions, and low textured regions that increase the error rate of tracking visual cues. A linear multi-orientation, multi-scale image decomposable steerable pyramid transformation was applied to images to overcome the above mentioned problems. Using the Steerable pyramid transform, depth of the image features was measured, features closer to camera were neglected based on the threshold value and pure background was extracted for further processing. This step enhances the speed due to less memory consumption, as most reliable feature vectors are commonly present at the static objects of the scene (background). Using Zero Normalized Cross Correlation, singular points containing reliable feature points were extracted. Further, the RANSAC method was used to eliminate the outliers effectively. Finally, Affine transformation was employed to derive the pure rotational and translational values of the camera. The developed algorithm was tested on different stereo sequences and the results were compared with its ground truth values. The results show a good accuracy and achieved 1.09% translational error with 0.027 deg/m rotational error at a speed of 1.96 s.
An Efficient Federated Convolutional Neural Network Scheme with Differential Privacy

Federated learning can complete the neural network model training without uploading users’ private data. However, the deep leakage from gradients (DLG) and the compensatory reconstruction attack (CRA) can reconstruct the training data according to the gradients uploaded by users. We propose an efficient federated convolutional neural network scheme with differential privacy to solve this problem. By adding Gaussian noise to the fully connected layers of the convolutional neural network, the attacker cannot identify the critical gradients that cause privacy leakage. The cumulative privacy loss is tracked using the analytical moments accountant technique. We conduct extensive experiments on the MNIST and CIFAR10 datasets to evaluate our defense algorithm. After selecting appropriate parameters, the results show that our defense algorithm can defend against DLG and CRA while maintaining a high model accuracy.
A Context-Guided Attention Method for Integrating Features of Histopathological Patches

Lots of researchers have studied for classifying histopathological whole slide images (WSIs). Since a WSI is too large to be processed directly, researchers usually cut it into many small-sized patches and then integrate the discriminative features extracted from the patches to obtain a slide-level feature of the WSI. The integration strategy generating the slide-level features is crucial for the WSI classification model. Lots of attention-based methods have been proposed for such purpose. However, most attention-based methods do not take the patches relationship into consideration, which affects the classification performance of the models. In this work, we propose a novel Context-Guided attention (CGattention) method to integrate the patch-level features, which constructs a context vector to simulate the global context information of the whole WSI and implicitly characterizes the relationship between patches in the WSI. When evaluated on two publicly available datasets, the CGattention based model obtained the better performance than other attention-based models.
Knowledge Enhanced Coreference Resolution via Gated Attention

Coreference resolution aims at linking all mentions that refer to the same entity, which are widely adopted in many biomedical and bioinformatics tasks, such as biomedical knowledge graph construction and metabolic pathway integration. Many recent studies focus on improving neural model structures. However, we argue that a practical method that integrates commonsense knowledge can further improve coreference resolution performance, because commonsense delivers extra prior knowledge for reasoning and can enhance related representations, rather than naive mention-context occurrence modeling. In this work, we propose an effective method to integrate external commonsense knowledge into a neural coreference resolution model. Specially, a gated attention mechanism is employed in our method to leverage commonsense according to different contexts. By using ConceptNet as the knowledge base in three span-ranking backbone models, the models can yield significant performance gains on used datasets. We also achieve improvements in tasks of long-term mention detection and cross-sentence coreferences after incorporating knowledge.
Distributed Data Minimization for Decentralized Collaborative Filtering Systems

Data minimization is a legal principle that mandates the limitation of personal data to a necessary minimum in order to protect the privacy of individuals. In this light, we address ourselves to decentralized collaborative filtering systems in which individual users interact with each other instead of institutional recommendation providers to obtain recommendations. Decentralized collaborative filtering systems, in which data is distributed over all users, bear a privacy benefit over traditional centralized systems in which data is kept at a single authoritative node. We propose the first formal definition of data minimization for use in distributed systems in order to combine the privacy benefits of data minimization and data distribution. On the basis of this definition, we propose a distributed data minimization scheme based on Distributed Gradient Descent (DGD) that solves the data minimization problem for decentralized collaborative filtering systems. Here, data minimization represents a collection minimization problem in which users aim to collect necessary minimum amounts of rating data from other users in the system. We find by means of simulation that the overall amount of data collected by all users can be reduced significantly (> 14.65%) without jeopardizing recommendation performance, when users coordinate data collection. We thus prove that users collect unnecessarily much data, when they do not coordinate data collection.
A New Machine Vision Method for Target Detection and Localization of Malleable Iron Pipes: An Experimental Case

Malleable iron pipes are widely used in construction, manufacturing, aerospace, and many other fields. Cast malleable iron pipes need to be treated flat to meet the needs of different shapes and sizes. This process is usually completed manually, which is low efficiency and is subject to potential safety risks. To solve this problem, a machine vision method is proposed to detect and localize malleable iron pipes. Point cloud images of malleable iron pipes are obtained by the Random Sample Consensus (RANSAC) algorithm, and precise matching is completed by the Iterative Closest Point (ICP) algorithm to obtain more accurate positions, so as to realize robot grasping. The grasping experiments of malleable iron pipes with the same and different specifications were carried out using a specially designed experimental platform. The results show that malleable iron pipes can be identified effectively and that the corresponding grasping success rate is more than 85 %. The target detection and localization method can obtain the three-dimensional (3D) position of malleable iron pipes to improve grasping efficiency, which provided a certain theoretical basis and guiding significance to improve production efficiency in practice.
An Improved Pairing-Free Ciphertext Policy Framework for IoT

Internet of Things (IoT) enables devices to get connected to the internet.Once they are connected, they behave as smart devices thereby releasing sensitive data periodically.There is a necessity to preserve the confidentiality and integrity of this data during transmission in public communication channels and also permitting only legitimate users to access their data A key challenge of smart networks is to establish a secure end-to-end data communication architecture by addressing the security vulnerabilities of data users and smart devices.The objective of this research work is to create a framework encompassing Ciphertext policy Attribute-based Encryption scheme using block encryption and BLAKE hashing technique.An improved Pairing-Free-Ciphertext policy Attribute-based encryption algorithm has been developed to overcome the aforementioned challenges.Further, a comparative study has been performed between the proposed scheme and the different encryption algorithms.It is found that the proposed scheme scores well over the already existing schemes.The scheme is evaluated in terms of execution time and communication overhead.The robustness of the proposed scheme is also analyzed from the perspective of several security goals.
An Enhanced Multi-Level Authentication Electronic Voting System

Originally, manual voting systems are surrounded with issues like results manipulation, errors and long result computation time, ineligible voters, void votes among others. Electronic voting system helped in overcoming the challenges with manual voting system, to engendered other problems of phishing, men in the middle attack alongside voter’s impersonation. By these challenges, the integrity of an election results in a distributed system has become another top concern for e-voting system based on reliability. To achieve an improved voters’ authentication and result validation with excellent user experience, here, a Facial Recognition Electronic Voting System that is power-driven by Blockchain Technology was developed. The entire election engineering activities are decentralised with improved security features to enhance transparency, verifiability, and accountability for each vote count. The self-service voting system was built by smart contract and implemented on the Ethereum network. The obtained reports and evaluations reflected a non-editable and self-sufficiently certifiable system for voting. It also has a competitive edge over fingerprint enabled e-voting system. Aside it’s excellent usability and general acceptance, the developed method discarded to a larger extend, intended fraudulent actions from election activities by eliminating the involvement of a middleman while facilitating privacy, convenience, eligibility and satisfactory voters’ right.
An Evaluation of a Ray-Tracing Based Model for Photorealistic Image Rendering of Confined Plasma in Stellarators

As the world moves away from traditional energy sources based on fossil fuels, several alternatives have been explored. One promising clean energy source is nuclear fusion. The fusion of hydrogen isotopes may provide generous consumable energy gains. However, nuclear fusion reactors are not ready to become a productive mechanism yet. To get a better understanding of plasma, numerical simulations and scientific visualizations over high-performance computing systems are mandatory. The results from the simulations and a proper display of the data are key to design and tune up nuclear fusion reactors. It is also thanks to the international collaboration effort such as the advisory contribution and tools of researchers from the Argonne National Laboratory in the United States in conjunction with the National Center for High Technology of Costa Rica that this work was successfully carried out. In a previous work, we explored a new approach of the scientific visualization of plasma confinement, presenting one model to generate realistic plasma representations. This work presents an evaluation of the expected quality of the images rendered with the created model. We propose a concept called visual plausibility as an evaluation attribute to rate each rendered image by physicists that already know about the plasma appearance.
Machine learning Scheme for Managing Virtual Computing Resources in Cloud Market

Cloud provider can maximize his profit while guaranteeing quality of service (QoS) required by clients. In this work, we propose new scheme for maximizing cloud provider profit while guaranteeing QoS for clients. Cloud provider (CP) manages all available resources on cloud market. The key objective of our scheme is extracting the optimal charging strategy for serving clients requests to maximize CP's profit by choosing proper set of requests to be served, subject to uncertain demand of service and time-varying service cost. As variety of clients classes submit their requests and offer different prices for service, this problem deserves special treatment. To tackle the uncertain service prices, Q-Iearning is proposed to extract the optimal charging policy. Numerical results show that the proposed scheme can improve the reward of CP significantly.
Enhanced 3D Residual Network for Human Fall Detection in Video Surveillance

In the public healthcare, a computational system that can automatically and efficiently detect and classify falls from a video sequence has significant potential.With the advancement of deep learning, which can extract temporal and spatial information, has become more widespread.However, traditional 3D CNNs that usually adopt shallow networks cannot obtain higher recognition accuracy than deeper networks.Additionally, some experiences of neural network show that the problem of gradient explosions occurs with increasing the network layers.As a result, an enhanced three-dimensional ResNet-based method for fall detection (3D-ERes-FD) is proposed to directly extract spatio-temporal features to address these issues.In our method, a 50-layer 3D residual network is used to deepen the network for improving fall recognition accuracy.Furthermore, enhanced residual units with four convolutional layers are developed to efficiently reduce the number of parameters and increase the depth of the network.According to the experimental results, the proposed method outperformed several state-of-the-art methods.
Reservoir Self Organizing Map

As IoT devices become more widespread, the load on cloud servers that process the time-series data acquired from IoT devices are expected to increase. For this reason, edge computing, in which data processing should be performed by IoT terminals and servers installed nearby, is attracting attention. It is difficult to use high-performance computers that can handle large amounts of computation on edge computing. As a solution to this problem, Reservoir Computing, a kind of recurrent neural network with low computational cost, was proposed. The purpose of this research is to improve the prediction accuracy of time series data by combining Reservoir Computing with SOM and changing the method for updating the weight vectors of output.
Surveying Semantic Segmentation Models Using Traffic Landmark Dataset

With recent advancements in the field of semantic and instance segmentation, computer vision and deep learning researchers managed to develop a wide range of solutions to achieve increasingly better results on different segmentation tasks. One of the possible applications that did not get as much attention from the research community as others, is the segmentation of traffic landmarks, such as crosswalks, lanes, stop lines, speed limits painted on the road etc. This particular type of application can explore new possibilities for the development of self-driving cars, assisted and semi-assisted driving, as well as traffic landmark maintenance and road safety. In this paper, we surveyed several segmentation models, in an attempt to provide an overview of the landscape of potential solutions for such problems, by contrasting and analyzing their performances on a dataset for traffic landmark detection, which we obtained from the local government facilities of Mie prefecture in Japan, and annotated as part of this work. To build a comprehensive survey, all while attempting to optimize the performance of our segmentation task, we obtained a set of 13000 high-resolution images and labeled them pixel-wise then we trained a set of well-known segmentation models such as FCN [1], U-Net [2], PSP-Net [3], DeepLabV3 [4], Mask-RCNN [5], Seg-Net [6], and Link-Net [7] with various back-bone structures. The results shown in table. I, and table. II, showed a clear advantage for U-Net models when it comes to Dice score [8] evaluation.
Open-SBS: Smart Building Simulator

With the rise of machine learning and deep learning techniques in recent years, a representative dataset has become an inspiring source of prediction and knowledge extraction. Furthermore, ambient intelligence environments have emerged as one of the most important research areas. Many complex tasks and challenges face this area to validate and evaluate field research. One of the major challenges in the smart building domain is the lack of a simulation tool capable of simulating complex contexts, executing semantic rules, and collecting representative datasets for experiments. To address these issues, we present in this paper a cross-platform, open-source Smart Building Simulator (Open-SBS). Open-SBS offer an opportunity for researchers in the field of Ambient Intelligence environments to simulate complex contexts and collect representative datasets, save and share their models of experiments. We propose a new simulation process that is divided into four distinct phases: designing, scenario creation, semantic rule addition, and data-set generation. On the System Usability Scale, we conducted a study to assess the ease of use of Open-SBS (SUS).
Network on Privacy-Aware Audio-and Video-Based Applications for Active and Assisted Living: GoodBrother Project

Active and Assisted Living (AAL) systems have a purpose to improve the lives of older or impaired people in various aspects. However, the use of equipment for data acquisition in these systems can be considered intrusive in some cases. Although de-identification may provide the needed protection to some extent, it is not always preferred, as it could affect the quality and utility of any obtained data. It is therefore crucial to establish methodologies for protecting the privacy of those monitored and thus affected by AAL systems. The purpose of GoodBrother is to a) analyze any issues arising from the use of monitoring AAL systems, regarding the users' privacy; b) establish proper guidelines for the use of these systems; c) develop privacy-aware methodologies for data handling; d) increase the systems' robustness and reliability; e) create databases to use towards benchmarking. Each one of these objectives are handled by separate interdisciplinary working groups.
SecDec: Secure Decode Stage thanks to masking of instructions with the generated signals

Physical attacks are becoming a major security issue in IOT applications. One of the main vectors of attacks on processors is the corruption of the execution flow. Fault injections allow the modification of instructions, in particular jumps and branches. The proposed approach involves making a RISC-V processor's instruction path more resistant by introducing dependencies between succeeding instructions. The signals extracted from the instruction decoding stage is used to unmask the following instruction. Whereas all instructions have been previously masked during compilation with the expected mask. We show that this solution has a very low hardware overhead of 3.25% and power consumption of 4.33%. But also overhead software of 1.61% in code size and 1.12% in execution time. An instruction corruption or a jump will be detected on average in fewer than 2 cycles after the fault while making disassembling from side-channel leakages becomes more difficult.
Design and Implementation of Traceability System Based on Blockchain

The fifth Plenary Session of the 19th CPC Central Committee included poverty alleviation and rural revitalization as the main goals of economic and social development during the 14th Five-Year Plan period. Moreover, it has created a new situation of digital sales of poverty alleviation and helps agricultural products such as "agriculture + e-commerce" and "agriculture + live streaming", and the traceability of agricultural products has attracted much attention. Com14bining the current social environment background and the investigation and understanding of the existing agricultural product sales mode of poverty alleviation, we found that the existing sales model chain and the traditional traceability system model have many shortcomings, such as unclear information sources and easy tampering. Based on this design, the idea of constructing a traceability system based on blockchain is proposed, and the required technical and economic feasibility is analyzed to confirm the realization of the idea. It is intended to meet the needs of agricultural product information management, poverty alleviation farmer information management, product traceability, etc. And through the web page and mobile terminal information interaction, the realization of agricultural products at any time, mobile, portable query. The final system test can ensure the normal operation of functions and the security, credibility, and transparency of information, provide new ideas and solutions for the field of targeted poverty alleviation, and help rural revitalization.
Implementation of the layout of the borrowing search system in text arrays

The article describes a common algorithm for match searching between the content of text arrays, which makes it possible to determine potential borrowings of texts with a high degree of probability using the example of analyzing a data bank of academic texts and an input text. The main ideas for the practical implementation of such a system are identified, as well as potential ways for further development to bring the described model to a state suitable for use by end users.
Selecting Reliable Instances from ImageNet for Medical Image Domain Adaptation

Pre-training deep learning models on ImageNet and transferring the models to medical image applications facilitate to improve the medical image analysis and reduce the need for labeled medical image data. However, some images from ImageNet may be fundamentally different from medical images in feature representation and lead to the negative transfer effects. To deal with this issue, we propose a novel strategy based on evidence theory to select reliable instances from ImageNet for medical image domain adaptation. Specifically, we formulate an evidential mass function to measure the ignorance and reliability of the images from ImageNet with respect to the classification tasks of medical images. Through selecting reliable instances with low ignorance degree from ImageNet, we can enhance the transfer performances of deep neural networks in medical image domain adaptation. Moreover, the proposed data selection strategy is independent of specific learning algorithm and can be viewed as a common preprocessing technique. Numerical experiments on tomography images, X-Ray images, and ultrasound images are given to comprehensively demonstrate the effectiveness of the selection strategy.