Reviewerly/exHarmony huggingface dataset:
Augmented semantic feature based generative network for generalized zero-shot learning

Zero-shot learning (ZSL) aims to recognize objects in images when no training data is available for the object classes. Under generalized zero-shot learning (GZSL) setting, the test objects belong to seen or unseen categories. In many recent studies, zero-shot learning is performed by leveraging generative networks to synthesize visual features for unseen class from class-specific semantic features. The user-defined semantic information is incomplete and lack of discriminability. However, most generative methods use user-defined semantic information directly as constraints of the generative model, which makes the visual features synthesized by the models lack of diversity and separability. In this paper, we propose a novel method to improve the semantic feature by utilizing discriminative visual features. Furthermore, a novel Augmented Semantic Feature Based Generative Network (ASFGN) is built to synthesize the separable visual representations for unseen classes. Since GAN-based generative model may suffer from mode collapse, we propose a novel collapse-alleviate loss to improve the training stability and generalization performance of our generative network. Extensive experiments on four benchmark datasets prove that our method outperforms the state-of-art approaches in both ZSL and GZSL settings.
Deep Heterogeneous Network for Temporal Set Prediction

Given historical sequential sets of elements such as purchasing items from time to time, receiving clinical records in subsequent visits to the hospitals, could be formalized as sequential sets, namely temporal sets. Forecasting the elements in the subsequent set according to historical sequence of sets is denoted as the problem of temporal sets prediction, which can provide useful and effective information for decision-making and planning in different domains. However, due to the difficulty of set representation, dynamic temporal dependence of historical sets and fusion of multi-level user preference , it is challenging to model and predict such temporal sets. To address these issues above, we propose a novel Deep Heterogeneous Network for Temporal Sets Prediction (DHNTSP) in this paper. Firstly, we design a set representation method based on Heterogeneous Information Network (HIN) embedding, where HIN is used to model the multiple relationships among sets, items, users and categories, and matrix factorization is used to vectorize the nodes of HIN. Then, an attention-based recurrent module is designed to learn the temporal dependence of the historical sets. Last, a fusion module is designed to combine sequential-level and individual-level of user preference to further improve the performance. The experiments are conducted on real-world datasets, and results demonstrate that DHNTSP outperforms both classical and the state-of-the-art methods.
Dialect Identification through Adversarial Learning and Knowledge Distillation on Romanian BERT

Dialect identification is a task with applicability in a vast array of domains, ranging from automatic speech recognition to opinion mining. This work presents our architectures used for the VarDial 2021 Romanian Dialect Identification subtask. We introduced a series of solutions based on Romanian or multilingual Transformers, as well as adversarial training techniques. At the same time, we experimented with a knowledge distillation tool in order to check whether a smaller model can maintain the performance of our best approach. Our best solution managed to obtain a weighted F1-score of 0.7324, allowing us to obtain the 2nd place on the leaderboard.
PCNet: partial convolution attention mechanism for image inpainting

Image inpainting is an essential issue in the field of computer vision. The basic purpose is to automatically recover the lost content according to the known content in the image. Although significant progress has been made in the completion of the regular missing image, the completion of the irregular image is still challenging. The completed images generated by the previous methods were different from the surrounding areas, considering the low efficiency of convolution in processing spatial position information. Based on this study, a partial convolution attention mechanism for image inpainting was proposed (PCNet), and an attention mechanism was introduced to extract long-distance and irregular image content which is a self-supervised module capable of focusing on global information. In addition, attentive normalization is introduced to model the long-distance relationship on the conditional image generation task. Experiments show that the results generated by our method are more natural and real, and the completed parts demonstrate more connected consistency.
Hybrid Secure Cloud Storage data based on improved Encryption Scheme

Cloud computing is a utility for data storage. Data storage security has become a primary challenge. The users can access, share & transacts the data as the cloud offers services based on the user demand. The Cloud data is originated from various sources, how secure the data is? Data security issues are increasing rapidly as data is flowing across the internet. To protect sensitive information there are many encryption techniques to hide the data from unauthenticated users. To secure the data encryption and decryption methods are used by which only authorized users can only retrieve the data. But sometimes Brute force method can recognize the hidden data. To enrich data confidentiality and authentication problems, a proposed method is used in which combination of AES and proxy re-encryption with Honey encryption is used. The system improves the data security for outsourced data. Honey encryption with hybrid cryptography can make unauthorized users to access only plausible looking messages.
Automated Online Experiment-Driven Adaptation–Mechanics and Cost Aspects

As modern software-intensive systems become larger, more complex, and more customizable, it is desirable to optimize their functionality by runtime adaptations. However, in most cases it is infeasible to fully model and predict their behavior in advance, which is a classical requirement of runtime self-adaptation. To address this problem, we propose their self-adaptation based on a sequence of online experiments carried out in a production environment. The key idea is to evaluate each experiment by data analysis and determine the next potential experiment via an optimization strategy. The feasibility of the approach is illustrated on a use case devoted to online self-adaptation of traffic navigation where Bayesian optimization, grid search, and local search are employed as the optimization strategies. Furthermore, the cost of the experiments is discussed and three key cost components are examined—time cost, adaptation cost, and endurability cost.
Real-time Object Detection with Deep Learning for Robot Vision on Mixed Reality Device

Mixed reality device sensing capabilities are valuable for robots, for example, the inertial measurement unit (IMU) sensor and time-of-flight (TOF) depth sensor can support the robot in navigating its environment. This paper demonstrates a deep learning (YOLO model) background, realtime object detection system implemented on mixed reality device. The goal of the system is to create a real-time communication system between HoloLens and Ubuntu systems to enable real-time object detection using the YOLO model. The experimental results show that the proposed method has a fast speed to achieve real-time object detection using HoloLens. This enables Microsoft HoloLens as a device for robot vision. To enhance human-robot interaction, we will apply it to a wearable robot arm system to automatically grasp objects in the future.
Study of Blockchain Technology in Empowering the SME

Recently, Blockchain technology has gained considerable attention from researchers and practitioners. This is mainly due to its unique features including decentralization, security, reliability, and data integrity. Despite this increasing interest, little is recognized and that over existing notion of knowledge and experience concerning the usages of Blockchain technology in education. The whole paper is a comparative study on Blockchain-based educational software. It concentrates on three major concepts: (1) academic apps formed with Blockchain, (2) the advantages which could be brought to learning by blockchain and approaches for implementing blockchain innovation in schools and (3) issues. A thorough review of the outcomes of every framework is carried out, as far as a thorough review is focused on the observations. The analysis also provides visibility into certain aspects of learning, which has been gained through blockchain innovation. During the past few years, the issue of funding small and medium-sized enterprises (SME) seems to have been a challenge for emerging nations in particular the Financial regions in developed countries and growing economies have often formed specialized target economies primarily reserved for SMEs in current history. The development about such enough prime businesses devoted to small and mediumsized enterprises is increasingly viewed as an option to the prevailing new investment. Financial leverage has grown differently in emerging markets yet certain issues continue unresolved. Blockchain has significant possibilities in the business sector. While such innovation will not be manipulated or interfered upon, it might be a benefit for junior market indexes although it is a reliable, effective and low-cost method for registering goods and purchase of commodities. Consequently, Blockchain technology optimizes the payment of funds and exchange of equity by mentoring the purchases among small and medium-sized enterprises or startups and shareholders.
An Artificial Orca Algorithm for Continuous Problems

In this paper, a very interesting species in its behaviour, social organization and also hunting/foraging techniques is mimicked in a new swarm intelligence algorithm. Several observations of Orcas in their living environment allows to model this species in a swarm optimization algorithm called Artificial Orca Algorithm (AOA). The algorithm proposes an original structure for its population and is modelled mathematically mimicking orca’s lifestyle on the intensification and diversification searches. The experimental study we conducted was tested on a benchmark used to evaluate the new swarm algorithm in a set of 21 mathematical optimization benchmark. The obtained results compared to GA(Genetic Algorithm), BA(Bat Algorithm), EHO(Elephant Herding Optimization), PSO(Particle Swarm Optimization) and WOA(Whale Optimization Algorithm) show that the proposed approach is efficient, which prompt to look further and test it on other optimization problem in order to benefit from the robustness of this latter. The source code of AOA is publicly available at: https://lria.usthb.dz/aoa.
Big Data: What are the Implications for Public Sector Policy in Society 5.0 Era?

Abstract Modernization in various sectors of life has made data and information valuable, which has an impact in changing people’s lives. One of the changes is how people get information from a large amount of data to be used in the public development sector. Public policy, as a solution to solve various public problems, needs to be supported by accurate data and information facts. With a massive amount of data, this has an impact on public sector organizations to be able to store and analyze various types of information for public policies consideration. The term ‘Society 5.0’ has emerged, a concept that refers to revolution in people’s lives through the use of technology by considering the humanities aspect. The potential for the huge benefits of big data technology is interesting to know as to whether big data can be used for the public policy formulation process. Therefore, this article will provide an overview of the implications of big data for the public policy process in society. This research uses qualitative method with literature study approach. The data came from websites and government documents related to the use of big data in public policies, analyzed using the NVivo12 Plus application. The emergence of big data, as a basis for making public policy, can be utilized for analysis, which includes social data analysis, historical data analysis, and predictive data analysis which can influence accuracy in the policy decision making. Public policy in the era of society 5.0 is vital to be realized through the use of big data as a source of information supported by technology and the readiness of actors in the policy-making process.
Research on Multi-Angle Face Detection Method Based on Improved YOLOV2 Algorithm

Abstract Face detection based on the YOLOV2 algorithm can achieve a higher level of accuracy and show a faster detection speed. Therefore, the YOLOV2 algorithm appears more frequently in the field of target real-time detection. The traditional YOLOV2 algorithm uses the Darknet-19 network. Based on this, this paper builds a new network by merging the residual network (ResNet). In addition, in order to further make up for the defects exposed by the YOLOV2 algorithm in face detection, we change the weight of the loss function and implement an image enhancement scheme on the training set. The YOLOV2 algorithm and the improved YOLOV2 algorithm are compared and tested on the Wider Face dataset, and then we use face data sets from different angles to test the performance of the algorithm. According to the analysis of the experimental results, the improved YOLOV2 algorithm can show higher accuracy and robustness in multi-angle face detection.
Semi-supervised deep learning approach to break common CAPTCHAs

Manual data annotation is a time consuming activity. A novel strategy for automatic training of the CAPTCHA breaking system with no manual dataset creation is presented in this paper. We demonstrate the feasibility of the attack against a text-based CAPTCHA scheme utilizing similar network infrastructure used for Denial of Service attacks. The main goal of our research is to present a possible vulnerability in CAPTCHA systems when combining the brute-force attack with transfer learning. The classification step utilizes a simple convolutional neural network with 15 layers. Training stage uses automatically prepared dataset created without any human intervention and transfer learning for fine-tuning the deep neural network classifier. The designed system for breaking text-based CAPTCHAs achieved 80% classification accuracy after 6 fine-tuning steps for a 5 digit text-based CAPTCHA system. The results presented in this paper suggest, that even the simple attack with a large number of attacking computers can be an effective alternative to current CAPTCHA breaking systems.
Block-cyclic stochastic coordinate descent for deep neural networks

We present a stochastic first-order optimization algorithm, named block-cyclic stochastic coordinate descent (BCSC), that adds a cyclic constraint to stochastic block-coordinate descent in the selection of both data and parameters. It uses different subsets of the data to update different subsets of the parameters, thus limiting the detrimental effect of outliers in the training set. Empirical tests in image classification benchmark datasets show that BCSC outperforms state-of-the-art optimization methods in generalization leading to higher accuracy within the same number of update iterations. The improvements are consistent across different architectures and datasets, and can be combined with other training techniques and regularizations.
Constrained Quantum Optimization Algorithm

Classical computing still suffers from the high computational complexity of finding the extreme value of a constrained goal function or an unordered database with respect to a certain constraint. Quantum computing offers new possibilities and solutions due to its quantum nature to surmount this problem. In this paper, we extended a new version of quantum existence testing (QET) _we call it a constrained quantum relation testing (CQRT)_ we also investigate analytically its classical and quantum certainty. Furthermore, we show how the CQRT can be applied for extending the quantum extreme value searching algorithm (QEVSA) version to a constrained quantum optimization algorithm (CQOA). And we gave an extensive explanation of how the CQOA works and how it finds the extreme constrained optimum value solution with high complexity reduction, maximum exponential speed, and high certainty.
Decentralized Voting Platform based on Ethereum Blockchain

In traditional voting schemes the integrity has always been questionable and it is also not feasible for some voters to vote because of inter-state residency. In order to solve this problem we are using distributed ledger technology to build the application for voting which is accessible to everyone and from everywhere. The application is based on the ethereum blockchain which is making the whole system secure and unaltered in nature, which means that the every vote is counted correctly and no fraud can be possible regarding the voting. This blockchain technology stores the data in blocks and it is nearly impossible to temper the data because of the interconnected blocks which are cryptographically secured. This system solves the problem of lack of trust and transparency.
Privacy-preserving Image Retrieval Scheme Allowing Mixed Use of Lossless and JPEG Compressed Images

We propose a novel content based-image retrieval scheme for encryption-then-compression (EtC) images. In conventional retrieval schemes for EtC images, the JPEG standard was applied to EtC images as a compression method, although EtC images can be applied to both JPEG compression and lossless compression methods such as JPEG-LS. In contrast, the proposed retrieval scheme allows the mixed-use of lossless and lossy-compression methods. In an experiment, the proposed method is demonstrated to maintain a high retrieval performance, even under the mixed-use of lossless- and lossy-compressed EtC images.
Time fractional evolution of a single quantum state and entangled state

Memory effect of time fractional Schrödinger equation plays a significant role in evolution of a single quantum state and quantum entanglement. We investigate the time fractional evolution of a single quantum state and entangled state respectively. Comparing to the results of standard Schrödinger equation, we find that the influence of memory action is unstable, which will change over time until the result is opposite to the initial effect. Our study might provide a new perspective on the role of time fractional Schrödinger equation.
Deep attributed network representation learning via attribute enhanced neighborhood

Attributed network representation learning aims at learning node embeddings by integrating network structure and attribute information. It is a challenge to fully capture the microscopic structure and the attribute semantics simultaneously, where the microscopic structure includes the one-step, two-step and multi-step relations, indicating the first-order, second-order and high-order proximity of nodes, respectively. In this paper, we propose a deep attributed network representation learning via attribute enhanced neighborhood (DANRL-ANE) model to improve the robustness and effectiveness of node representations. The DANRL-ANE model adopts the idea of the autoencoder, and expands the decoder component to three branches to capture different order proximity. We linearly combine the adjacency matrix with the attribute similarity matrix as the input of DANRL-ANE, where the attribute similarity matrix is calculated by the cosine similarity between the attributes based on the social homophily. Moreover, the sigmoid cross-entropy loss function is extended to capture the neighborhood character, so that the first-order proximity could be well-preserved. We compare our model with the state-of-the-art models, especially, the latest graph autoencoders (GAEs) method, ARGA, and demonstrate the contribution of each module on real-world datasets and two network analysis tasks, i.e., link prediction and node classification. The DANRL-ANE model performs well on various networks, even on sparse networks or networks with isolated nodes when the attribute information is sufficient.
Pyramid convolutional network for colorization in monochrome-color multi-lens camera system

We study the problem of colorization in the monochrome-color multi-lens camera system. The recent convolutional network (CNN) based method learns a 3-D weight volume to solve this problem and gets very high accuracy. But the model size is very big due to the large-displacement problem, i.e. there are large displacements between some pixels in the input gray image and the pixels in the reference image that could provide correct colors. To overcome the limitations, we improve the recent CNN based method and propose to combine pyramid processing with CNNs for colorization. At each level of the pyramid, our method warps the reference image using the estimated warping information map from the previous level so that we can learn a much more compact 3-D weight volume for colorization. We also compute an update to the warping information map by a Markov Random Field method at each level. With the pyramid CNN structure, our model has much smaller model size, and experimental results show that our method outperforms all of the state-of-the-art methods in accuracy as well.
Direction-aggregated Attack for Transferable Adversarial Examples

Deep neural networks are vulnerable to adversarial examples that are crafted by imposing imperceptible changes to the inputs. However, these adversarial examples are most successful in white-box settings where the model and its parameters are available. Finding adversarial examples that are transferable to other models or developed in a black-box setting is significantly more difficult. In this article, we propose the Direction-aggregated adversarial attacks that deliver transferable adversarial examples. Our method utilizes the aggregated direction during the attack process for avoiding the generated adversarial examples overfitting to the white-box model. Extensive experiments on ImageNet show that our proposed method improves the transferability of adversarial examples significantly and outperforms state-of-the-art attacks, especially against adversarial trained models. The best averaged attack success rate of our proposed method reaches 94.6% against three adversarial trained models and 94.8% against five defense methods. It also reveals that current defense approaches do not prevent transferable adversarial attacks.
Scalable Blockchain Framework for a Food Supply Chain

Of late, in a food supply chain (FSC) management, many incidents related to the mislabeling and mishandling of food items are found to occur frequently, which often leaves the customers with a question of how safe and reliable is the food they buy and consume. Since the information regarding the tracking of food items is distributed across different locations widely, and the data is vulnerable to being recorded wrongly, the reliability of tracking of the food items through FSC is suspected. Further, an FSC has various stakeholders that interact and transact continuously. Thus, the scalability issue also plays a vital role in making FSC more efficient. The present-day traceability solutions lack efficiency in terms of scalability and reliability. The scalability can be categorized as throughput, cost, capacity, and response time. In this paper, the proposed method for the traceability solution of a food supply chain (FSC) is based on Blockchain, which plays a vital role in providing transparency and integrity along with other salient features like decentralization, immutability, and verifiability. The proposed FSC is made scalable in terms of throughput and cost using state channeling off-chain algorithm for Blockchain implementation.