Reviewerly/exHarmony huggingface dataset:
Blockchain solution to improve trust in crowdfunding systems

research-article Share on Blockchain solution to improve trust in crowdfunding systems Authors: Imane Ezzine Mohammadia School of engineers, Mohammed V University in Rabat, Morocco Mohammadia School of engineers, Mohammed V University in Rabat, MoroccoView Profile , Laila Benhlima Mohammadia School of engineers, Mohammed V University in Rabat, Morocco Mohammadia School of engineers, Mohammed V University in Rabat, MoroccoView Profile Authors Info & Claims ArabWIC 2021: The 7th Annual International Conference on Arab Women in Computing in Conjunction with the 2nd Forum of Women in ResearchAugust 2021 Article No.: 14Pages 1–6https://doi.org/10.1145/3485557.3485572Online:29 November 2021Publication History 0citation36DownloadsMetricsTotal Citations0Total Downloads36Last 12 Months36Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Reconstruction of Hidden States in Stochastic Neural Field Equations with Infinite Signal Transmission Rate

Due to a growing interest in using dynamic neural field models for solving practical applications, a special interest lies in developing efficient estimation methods for accurate restoration of hidden processes of mathematical models at hand. In this paper, we first propose a constructive way of setting up the nonlinear Bayesian filtering problem arisen in Galerkin-type spectral approximations applied to dynamic neural fields. Next, we propose the efficient numerical scheme for reconstructing the neural population activity over the spatial and time domains from incomplete measurements in both domains. The proposed numerical scheme is designed using the Galerkin approximation and the continuous-discrete extended Kalman filter. The accuracy of the estimation procedure is evaluated by using artificial data through exhaustive numerical tests.
Coarse-to-Fine Evolutionary Method for Fast Horizon Detection in Maritime Images

Horizon detection is useful in maritime image processing for various purposes, such as estimation of camera orientation, registration of consecutive frames, and restriction of the object search region. Existing horizon detection methods are based on edge extraction. For accuracy, they use multiple images, which are filtered with different filter sizes. However, this increases the processing time. In addition, these methods are not robust to blurting. Therefore, we developed a horizon detection method without extracting the candidates from the edge information by formulating the horizon detection problem as a global optimization problem. A horizon line in an image plane was represented by two parameters, which were optimized by an evolutionary algorithm (genetic algorithm). Thus, the local and global features of a horizon were concurrently utilized in the optimization process, which was accelerated by applying a coarse-to-fine strategy. As a result, we could detect the horizon line on high-resolution maritime images in about 50ms. The performance of the proposed method was tested on 49 videos of the Singapore marine dataset and the Buoy dataset, which contain over 16000 frames under different scenarios. Experimental results show that the proposed method can achieve higher accuracy than state-of-the-art methods.
Experimental certification of the steering criterion based on the local uncertainty relation

Quantum nonlocality is a prominent discrepancy when we compare quantum physics with classical physics, and it manifests in different forms, one of which is quantum steering. Quantum steering refers to a correlation produced from a bipartite entangled state between the local measurement outcomes of one party and the postmeasurement states of the other. Combining with uncertainty relation, local measurements can be used to distinguish whether a quantum state is steerable. Thus, this method can be treated as an effective steering criterion. In this work, we certify this steering criterion based on the local uncertainty relation by an all-optical setup with a mixed state which consists of two Bell-like states. Furthermore, we also testify that the quantum steering criterion based on the local uncertainty relation is not only superior to the linear steering criterion but also superior to the steering criterion based on the entropic uncertainty relation in our scenario. Our efforts may promote the use of this criterion in quantum information processing tasks.
On the Performance of Deep Learning in the Full Edge and the Full Cloud Architectures

Deep learning today surpasses various machine learning approaches in performance and is widely used for variety of different tasks. Deep learning has increased accuracy compared to other approaches for tasks like language translation and image recognition. However, training a deep learning model on a large dataset is a challenging and expensive task that can be time consuming and require large computational resources. Therefore, Different architectures have been proposed for the implementation of deep learning models in machine vision systems to deal with this problem. Currently, the application of deep learning in the cloud is the most common and typical method. Nevertheless, the challenge of having to move the data from where it is generated to a cloud data center so that it can be used to prepare and develop machine learning models represents a major limitation of this approach. As a result, it is becoming increasingly important to consider moving aspects of deep learning to the edge, instead of the cloud, especially with the rapid increase in data volumes and the growing need to act in real time. From this perspective, a comparative study between the full edge and the full cloud architectures based on the performance of the deep learning models implemented in both architectures is elaborated. The results of this study lead us to specify the strengths of both the cloud and the edge for deploying deep learning models, and to choose the optimal architecture to deal with the rapid increase in data volumes and the growing need for real-time action.
HMS-OS: Improving the Human Mental Search Optimisation Algorithm by Grouping in both Search and Objective Space

The human mental search (HMS) algorithm is a relatively recent population-based metaheuristic algorithm, which has shown competitive performance in solving complex optimisation problems. It is based on three main operators: mental search, grouping, and movement. In the original HMS algorithm, a clustering algorithm is used to group the current population in order to identify a promising region in search space, while candidate solutions then move towards the best candidate solution in the promising region. In this paper, we propose a novel HMS algorithm, HMS-OS, which is based on clustering in both objective and search space, where clustering in objective space finds a set of best candidate solutions whose centroid is then also used in updating the population. For further improvement, HMS-OS benefits from an adaptive selection of the number of mental processes in the mental search operator. Experimental results on CEC-2017 benchmark functions with dimensionalities of 50 and 100, and in comparison to other optimisation algorithms, indicate that HMS-OS yields excellent performance, superior to those of other methods.
BITS: A Blockchain-Based Intelligent Teaching System for Smart Education

With the vigorous development of smart education, blockchain has attracted widespread attention from the industry and education circles due to its characteristics of being decentralized, immutable, and traceable. Using advanced blockchain technologies to help cultivate innovative computer professionals is vital to the national talent development strategy. This paper targets the shortcomings of talent training modes in colleges and universities and proposes a blockchain-based intelligent teaching system for smart education, named BITS for short. The BITS first sets up a blockchain integrated intelligent education platform for the learning, innovation, and output stages of talent cultivation. Secondly, it proposes to effectively enhance students' practical innovation ability through the full-cycle participation of in-school and corporate tutors in the student training process. By applying big data analysis and artificial intelligence techniques, BITS mines the inherent features of blockchain data and builds multi-dimensional student competence portraits in terms of professional literacy, development literacy, and humanistic literacy. Finally, our proposed BITS leverages the advantages of blockchain consensus, privacy, and trust to provide a diversified teaching feedback mechanism in which the feedback comes from in-school students, graduates, and third parties, leading to a more open and credible ecological environment for education.
Energy Minimization in a Sustainably Developed Environment Using Cloud Computing

The development in cloud computing increased the consumption of energy in processing and storing data. Cloud computing provides services that are provided by data centers that are dispersed throughout the world which can handle small to thousands of servers. This causes an increase in the carbon footprint which is very hazardous to the environment. Thus the concept of green IT has been introduced which can save the surroundings from carbon emission. The main aim of GCC is to decrease the content of carbon footprint in the data centers by decreasing the energy consumed by them. GCC is a bright and highly opted field of research as there is a need for stable and everlasting resources and their conservation hence by improving the consumption of computing resources from various aspects like software optimization, network optimization, and hardware optimization techniques. This chapter provides an overview of the necessity of employing a sustainable cloud computing system.
RETRACTED: The Exploration and Countermeasures of University Education Management Model Based on Big Data Technology

Abstract In the context of the current rapid development of big data technology, my country’s higher education management model has been greatly impacted. The reform of the student education management model in colleges and universities has both positive and negative aspects. This article makes relevant countermeasures to the transformation of college student education management mode in the era of big data, which is also one of the main research contents of this article. This article mainly discusses the teaching management mode of colleges and universities. It first introduces the changes and current situation of the education management mode, and then makes relevant countermeasures to analyze the current situation after the reform. Under this new situation, the development goals of college education management models have also changed, so a new higher-level development goal of college education management under the background of big data is outlined. This article applies the changes of big data in the education management model. The traditional education management model must be difficult to meet the actual needs of the development of the current big data era. Therefore, based on this, this article needs to combine the background of the rapid development of the current era, increase the frequency of communication between students and teachers, improve the professional quality of teachers themselves, actively innovate the theoretical needs of current education management, and establish a more professional education management team. Based on the above-mentioned problems, a combination of machine learning algorithms and electronic icon detection algorithms is proposed. Experimental research results show that there are many problems in the work of traditional university education management mode. The rise of big data has brought severe impact and challenges to it, and it also brought unprecedented opportunities to university education management.
Digital Watermarking System for Hard Cover Objects Against Cloning Attacks

We consider an application of digital watermark system technique for hard carriers (paper or plastic cover objects in addition). Algorithms of watermark embedding and extraction are proposed and the corresponding error probabilities of the extracted bits both for informed and blind decoders are presented. The spread spectrum signals used for embedding of watermark are optimized on their parameters. Similar experimental results are presented for data matrices carriers depending on paper sizes of data matrix copies. A protection against so-called “cloning” attack is elaborated where certificates are copied by attack scanner or photo camera and next printed and fixed as forges. The formulas for a missing the cloning attack and false alarm probabilities are proved. A full-scale experiment with a real scanner and printer confirms that the reliability of cloning attack detection can be provided under appropriate selection of watermark system parameters.
A Machine Learning Whale Algorithm Applied to the Matrix Covering Problem

In the industry, the need for optimization naturally arises, with which there is a large number of optimization problems, particularly combinatorial and NP-hard type. Therefore, there is an important motivation for the development of algorithms that address these types of problems. IN this line, a large number of metaheuristic algorithms have been developed which work only in continuous spaces. Modifying the latter in order to address combinatorial problems has important applications. In this article, we will study a general binarization mechanism of continuous metaheuristics based on clustering techniques and it will be applied to the whale algorithm. Experiments are designed in order to demonstrate the contribution of the clustering technique in the binarization process. The results indicate that the Ballena binary optimization algorithm (MLWH) obtains adequate results when it is evaluated with a combinatorial problem such as the SCP.
Deep Siamese Metric Learning: A Highly Scalable Approach to Searching Unordered Sets of Trajectories

This work proposes metric learning for fast similarity-based scene retrieval of unstructured ensembles of trajectory data from large databases. We present a novel representation learning approach using Siamese Metric Learning that approximates a distance preserving low-dimensional representation and that learns to estimate reasonable solutions to the assignment problem. To this end, we employ a Temporal Convolutional Network architecture that we extend with a gating mechanism to enable learning from sparse data, leading to solutions to the assignment problem exhibiting varying degrees of sparsity. Our experimental results on professional soccer tracking data provides insights on learned features and embeddings, as well as on generalization, sensitivity, and network architectural considerations. Our low approximation errors for learned representations and the interactive performance with retrieval times several magnitudes smaller shows that we outperform previous state of the art.
An Improved Dynamic Step Size RRT Algorithm in Complex Environments

Rapidly exploring Random Tree(RRT) is an efficient path planning algorithm based on random sampling, which plays an important role in the robot field and autonomous driving field. However, due to the randomness of sampling, its results are usually not optimal. This paper proposes a dynamic step size RRT algorithm, which mainly improves the traditional RRT as follows. First, combined with the Artificial Potential Field(APF), the target makes heuristic guidance for the sampling process. And then, the step size is adaptively changed according to the density of obstacles. After that, a one-shot heuristic strategy is used to speed up the search process. Finally, a bi-directional pruning strategy is adopted to reduce the path length by merging points. The simulation results show that the improved RRT algorithm can find the target faster and better.
Gesture recognition matching based on dynamic skeleton

Gestures, as a basic human feature, occupy an important position in human-computer interaction and other fields as well. In order to accurately recognize gestures and eliminate environmental interference, this paper proposes a gesture recognition matching method based on dynamic bones. This method uses the Mask R-CNN model and exponential filtering to identify and calibrate the key points of the hand. Through the segmentation and feature extraction of real-time frame images, the combined network is used to obtain stable and accurate hand bone key points. Based on the idea of constructing a Spatio-temporal graph convolutional network model based on ST-GCN, a skeletal point information gesture data set is constructed and sent to the network for training. Finally, template matching is used to realize gesture recognition. The experimental results show that the method can eliminate the environmental interference to the greatest extent, as well as the incomplete traditional data set, and the model accuracy defects caused by the lack of special samples. The recognition accuracy of the Chinese sign language database can reach 87.02%. Compared with previous researches on gesture recognition, it has improved accuracy and robustness.
A zero-knowledge proof system for OpenLibra

Zero-knowledge proofs (ZKPs) have garnered much attention recently. They have witnessed applicability in anonymizing tokens (e.g., Tornado Cash) and increasing transaction throughput (e.g., ZK-Rollups) for blockchains backing cryptocurrencies. ZKPs are touted to offer great potential for future blockchain applications that operate in a priori trustless environments across administrative boundaries. This short note discusses the integration of the ZK-STARK-based proof system into the OpenLibra blockchain. We generically describe the interaction of the involved ZKP capabilities with OpenLibra and showcase an open-sourced proof-of-concept implementation.
Privacy-Preserving Automatic Slipping Detection Method for Elderly in Bathroom Using Depth Sensors

Slipping is one of the main factors affecting the health of the elderly, especially in the humid bathroom environment. Failure to detect the elderly slipping in time may lead to more serious consequences. However, due to the privacy of the bathroom, it is not feasible to use ordinary video surveillance methods to monitor the elderly slipping in real time. In this research, we propose a slipping detection method based on depth sensors, which gets rid of the dependence on the video surveillance, can automatically detect the elderly slips and issue an alarm. First, based on the data collected by the depth sensors, we constructed a 3D scene point cloud representation without privacy information. Then, a pre-trained three-dimensional human body detection model is used to detect the accurate position of the human body. Finally, according to the coordinates of the 3D bounding box of the human body, it is judged whether the elderly has slipped. After verification on actual data sets, we achieved an accuracy rate of 98.4%, which not only ensures the privacy of the elderly, but also realizes timely slip alarm.
Privacy-Preserving Mechanisms with Explainability in Assistive AI Technologies

With the developing technology and increasing amount of data, Artificial Intelligence (AI) shows the effect in almost every field of our lives. Thanks to AI systems that are revolutionizing the technology world, things only humans could do before can be automated such as visual perception, decision-making, speech recognition, and translating between languages. Even though AI brings to mind the perception of a futuristic future, it also plays a big role in facilitating human life. This role is vital especially for people with disabilities trying to adapt to daily life and be fulfilled with the Assistive Technologies domain. In Model ICT Accessibility Policy Report (Available: https://www.itu.int/en/ITU-D/Digital-Inclusion/Persons-with-Disabilities/Documents/ICT%20Accessibility%20Policy%20Report.pdf . Accessed: 31 Jan 202 [1]), the Assistive Technologies are described as any information and communications technology used to protect, increase, or advance the functional abilities of individuals with particular needs or disabilities. But this brings the potential for simultaneous privacy issues such as spying, exploitation, and data breaches due to context-awareness interfaces and penetrable information everywhere. According to WHO (World Health Organization)’s report on disability, approximately 15% of the world’s population suffers from some form of disability (Summary World Report on Disability, Available: https://apps.who.int/iris/bitstream/handle/10665/70670/WHO_NMH_VIP_11.01_eng.pdf;jsessionid=50C7F4199A25E26711B5A903759B35C6?sequence=1 . Accessed: 31 Jan 2021 [2]). Considering that people with disabilities as well as many people in need to use these technologies, we can say that the number of potential users is much higher. Since they are designed to be used by vulnerable individuals with physical and cognitive disabilities, ensuring data privacy is of greater importance. While sensitive data needs to be protected, on the other hand, the use of this data is very critical for the functionality of technology. Besides all these, when decisions of AI systems affect humans’ lives, the need for figuring out how such decisions are taken comes out (Goodman B, Flaxman S, European union regulations on algorithmic decision-making and a right to explanation. AI Magazine 38 (3) 50–57 [3]). Especially with the development of deep neural networks which are considered as black-box models in recent years (Castelvecchi in Nature 538:20–23 [4]), the importance of explainability in Artificial Intelligence systems has come to the fore. In the literature, model explainability and privacy/security by design come together under the notion of Responsible AI (Arrietaa et al. in Information Fusion 58:82–115 [5]). In the scope of this chapter, privacy-preserving solutions for assistive technologies and the relationship between explainability and privacy will be tackled.
Analyzing Static Analysis Metric Trends towards Early Identification of Non-Maintainable Software Components

Nowadays, agile software development is considered a mainstream approach for software with fast release cycles and frequent changes in requirements. Most of the time, high velocity in software development implies poor software quality, especially when it comes to maintainability. In this work, we argue that ensuring the maintainability of a software component is not the result of a one-time only (or few-times only) set of fixes that eliminate technical debt, but the result of a continuous process across the software’s life cycle. We propose a maintainability evaluation methodology, where data residing in code hosting platforms are being used in order to identify non-maintainable software classes. Upon detecting classes that have been dropped from their project, we examine the progressing behavior of their static analysis metrics and evaluate maintainability upon the four primary source code properties: complexity, cohesion, inheritance and coupling. The evaluation of our methodology upon various axes, both qualitative and quantitative, indicates that our approach can provide actionable and interpretable maintainability evaluation at class level and identify non-maintainable components around 50% ahead of the software life cycle. Based on these results, we argue that the progressing behavior of static analysis metrics at a class level can provide valuable information about the maintainability degree of the component in time.