Reviewerly/exHarmony huggingface dataset:
TinyML Platforms Benchmarking

Recent advances in state-of-the-art ultra-low power embedded devices for machine learning (ML) have permitted a new class of products whose key features enable ML capabilities on microcontrollers with less than 1 mW power consumption (TinyML). TinyML provides a unique solution by aggregating and analyzing data at the edge on low-power embedded devices. However, we have only recently been able to run ML on microcontrollers, and the field is still in its infancy, which means that hardware, software, and research are changing extremely rapidly. Consequently, many TinyML frameworks have been developed for different platforms to facilitate the deployment of ML models and standardize the process. Therefore, in this paper, we focus on benchmarking two popular frameworks: Tensorflow Lite Micro (TFLM) on the Arduino Nano BLE and CUBE AI on the STM32-NucleoF401RE to provide a standardized framework selection criterion for specific applications.
Ensemble Models for Neural Source Code Summarization of Subroutines

A source code summary of a subroutine is a brief description of that subroutine. Summaries underpin a majority of documentation consumed by programmers, such as the method summaries in JavaDocs. Source code summarization is the task of writing these summaries. At present, most state-of-the-art approaches for code summarization are neural network-based solutions akin to seq2seq, graph2seq, and other encoder-decoder architectures. The input to the encoder is source code, while the decoder helps predict the natural language summary. While these models tend to be similar in structure, evidence is emerging that different models make different contributions to prediction quality - differences in model performance are orthogonal and complementary rather than uniform over the entire dataset. In this paper, we explore the orthogonal nature of different neural code summarization approaches and propose ensemble models to exploit this orthogonality for better overall performance. We demonstrate that a simple ensemble strategy boosts performance by up to 14.8%, and provide an explanation for this boost. The takeaway from this work is that a relatively small change to the inference procedure in most neural code summarization techniques leads to outsized improvements in prediction quality.
EntangleVR: A Visual Programming Interface for Virtual Reality Interactive Scene Generation

Entanglement is a unique phenomenon in quantum physics that describes a correlated relationship in the measurement of a group of spatially separated particles. In the fields of science fiction, game design, art and philosophy, it has inspired the creation of numerous innovative works. We present EntangleVR, a novel method to create entanglement-inspired virtual scenes with the goal to simplify representing this phenomenon in the design of interactive VR games and experiences. By providing a reactive visual programming interface, users can integrate entanglement into their design without requiring prior knowledge of quantum computing or quantum physics. Our system enables fast creation of complex scenes composed of virtual objects with manipulable correlated behaviors.
Experiments Focused on Exploration in Deep Reinforcement Learning

Automation with deep neural networks is popular topic. Personal artificial assistance, self driving cars and recommendations algorithms, they all use reinforcement learning. However, there are many question about this technology still unanswered. Can the learning process be accelerated, why sometimes the automatons are learning wrong behavior, is it possible for machines teach each others. This papers try to answer those questions with experiments.
The proposition of Process flow model for Scrum and eXtreme Programming

To carry out a quality project, the company collaborators need to rely on an efficient management methodology. It is the key to organize the project in a structured and streamlined manner, it helps the team to be more structured. The traditional methods are usually used on projects based on very precise ideas with very detailed specifications and planning which anticipated all possible risks. Nevertheless, these do not allow any kind of interaction with the customer, who will receive the project once it is finished. For that reason, solutions like eXtreme Programming and Scrum have been proposed by project management experts respecting the interaction with the customer, ability to change, and so on. These methods share the same values and principles, but each one has its own singularities. In this paper, we will present the process flow model of the Scrum and XP methods. For this process flow model, we used the BPMN models.
Hybrid image recommendation algorithm combining content and collaborative filtering approaches

The paper relates to the subject field of image recommender systems. The proposed approach of automatic image recommendation addresses the following shortcomings of existing solutions: manual input of metadata by users, the lack of user rating history consideration, significant computational resources. The main idea of the proposed approach is to recognize object classes from images using a convolutional neural network to make recommendations. In the proposed solution users and images are located in the semantic space represented by a graph. Software implementation of the proposed approach and obtained results are considered.
Vertical Federated Learning for Privacy-Preserving ML Model Development in Partially Disaggregated Networks

We present a novel framework that enables vendors and operators, with partial access to operational and monitoring features of a service, to collaboratively develop a ML-assisted solution without revealing any business-critical raw data to each other. We validate our proposal for a QoT estimation use-case.
Geometric feature statistics histogram for both real-valued and binary feature representations of 3D local shape

3D local feature description is now at the core of many 3D vision technologies. However, most of the existing 3D feature descriptors can't strike a balance among descriptiveness, robustness, compactness, and efficiency. To overcome the challenges, we propose a real-valued 3D local feature descriptor named Geometric Feature Statistics Histogram (GFSH) and its binary extension descriptor named B-GFSH. A GFSH descriptor first constructs an improved-weighted covariance matrix to solve a stable and reliable Local Reference Frame (LRF), and then achieves a comprehensive description of the 3D local surface by performing statistics on multiple geometric distribution features, namely voxel density, voxel centroid, and projection density. A particular trait of our GFSH descriptor is its seamless extension to the binary representation to reduce storage consumption and accelerate feature matching. For each sub-feature of GFSH, B-GFSH respectively adopts the corresponding binarization strategy, i.e., improved Gray code quantization, thresholding based on coordinates, and neighbor comparison. Extensive experiments on six public datasets prove that both GFSH and B-GFSH have high descriptiveness, strong robustness, and fast real-time performance. In addition, B-GFSH further has the characteristics of fast matching speed, low memory footprint, and high compactness. Finally, we conduct 3D scene registration and 3D object recognition experiments to visually demonstrate the actual effectiveness of GFSH and B-GFSH.
A Novel Fingerprinting Technique for Data Storing and Sharing through Clouds

With the emerging growth of digital data in information systems, technology faces the challenge of knowledge prevention, ownership rights protection, security, and privacy measurement of valuable and sensitive data. On-demand availability of various data as services in a shared and automated environment has become a reality with the advent of cloud computing. The digital fingerprinting technique has been adopted as an effective solution to protect the copyright and privacy of digital properties from illegal distribution and identification of malicious traitors over the cloud. Furthermore, it is used to trace the unauthorized distribution and the user of multimedia content distributed through the cloud. In this paper, we propose a novel fingerprinting technique for the cloud environment to protect numeric attributes in relational databases for digital privacy management. The proposed solution with the novel fingerprinting scheme is robust and efficient. It can address challenges such as embedding secure data over the cloud, essential to secure relational databases. The proposed technique provides a decoding accuracy of 100%, 90%, and 40% for 10% to 30%, 40%, and 50% of deleted records.
Adversarial Machine Learning for Protecting Against Online Manipulation

Adversarial examples are inputs to a machine learning system that result in an incorrect output from that system. Attacks launched through this type of input can cause severe consequences: for example, in the field of image recognition, a stop signal can be misclassified as a speed limit indication. However, adversarial examples also represent the fuel for a flurry of research directions in different domains and applications. Here, we give an overview of how they can be profitably exploited as powerful tools to build stronger learning models, capable of better-withstanding attacks, for two crucial tasks: fake news and social bot detection.
Organizational Building Blocks for Blockchain Governance: A Survey of 241 Blockchain White Papers

Governance for centralized organizational structures has long roots and well-developed frameworks, including for various specialty areas, such as IT or data governance. However, the introduction of blockchain technology as a supportive tool for implementing decentralized organizations requires a renewed focus for research in the area. The paper utilizes empirical data from blockchain ecosystems in the form of white papers (public communique of intention) to analyze their governance intentions. The empirical findings are based on a review of 241 blockchains and distributed ledger technology white papers, out of which 67 include explicit descriptions of how governance should be organized in the ecosystem. Our empirical research distinguishes between three categories of governance: objectives, mechanisms, and stakeholders. We further identify 28 features for these categories, which are described in an open encoding format. Hence, the paper contributes to the emerging blockchain research field, particularly to the decentralized aspects of blockchain governance research. This research also reveals that blockchain governance does not receive the attention it should as a large majority of ecosystems have not disclosed their governance intentions. The results can be utilized as a framework for future research. The results can also be helpful for industry when designing and developing governance systems.
On-Air Hand-Drawn Doodles for IoT Devices Authentication During COVID-19

In this paper, a new natural human interaction authentication method is proposed for Internet of Things (IoT) devices.In this method, the user draws a doodle on the air for authentication.On-air drawing refers to virtually drawing free hand-drawn doodle passwords through hand gestures on the air without touching anything that is recommended during COVID-19.This study uses the Google Quick Draw Doodle dataset for password doodles.The proposed method is based on a typical video camera, two lightweight convolutional neural networks (CNNs) and a Kalman filter.The first CNN for hand gesture classification was used to overcome dynamic hand gesture challenges on the air.Second CNN for authentication verification.A Kalman filter was used to correct and smooth the path drawn on the air.Two main goals must be achieved to accept the new authentication method: usability and security.The usability evaluation was based on the ISO 9241-11:2018 standard usability model.The results revealed that the accuracy of the proposed authentication method was 95%, efficiency was 94%, and user satisfaction was acceptable.The evaluation of security was based on two threats related to IoT devices: guessing and physical observation.The results show that the password strength of the proposed authentication method is stronger than the traditional 4-digits PIN password.The proposed authentication method is also resistant to physical observation threats.
What influences employees to follow security policies?

Data and information play a critical role in cyber security policies in organizations, so it is essential to protect them. Companies invest in the latest and most secured softwares, and specialized IT employees to protect the firm's data and information. Nevertheless, there are still incidents and huge losses of information because of the careless behaviour of the employees. Companies continue to spend considerable sums on security and yet remain in danger of attacks. By combining the value of congruence model (VC), the theory of planned behaviour model (TPB), and security conscious care behaviour we show that security behaviour can be influenced through effortless and low-cost measures that are a very advantageous solution for companies to protect their assets. With a sample of 193 respondents we demonstrate that companies need only to keep their employees motivated, happy, and satisfied in order to encourage them to adhere to the cyber security policies already in place. We show that information security awareness moderates the relationship between subjective norms and behaviour intention, and that job satisfaction moderates the relationship between behaviour intention and security behaviour. We show practical and theoretical implications based on our conclusions.
Self-Supervised Symmetric Nonnegative Matrix Factorization

Symmetric nonnegative matrix factorization (SNMF) has demonstrated to be a powerful method for data clustering. However, SNMF is mathematically formulated as a non-convex optimization problem, making it sensitive to the initialization of variables. Inspired by ensemble clustering that aims to seek a better clustering result from a set of clustering results, we propose self-supervised SNMF (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> NMF), which is capable of boosting clustering performance progressively by taking advantage of the sensitivity to initialization characteristic of SNMF, without relying on any additional information. Specifically, we first perform SNMF repeatedly with a random positive matrix for initialization each time, leading to multiple decomposed matrices. Then, we rank the quality of the resulting matrices with adaptively learned weights, from which a new similarity matrix that is expected to be more discriminative is reconstructed for SNMF again. These two steps are iterated until the stopping criterion/maximum number of iterations is achieved. We mathematically formulate S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> NMF as a constrained optimization problem, and provide an alternative optimization algorithm to solve it with the theoretical convergence guaranteed. Extensive experimental results on 10 commonly used benchmark datasets demonstrate the significant advantage of our S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> NMF over 14 state-of-the-art methods in terms of 5 quantitative metrics. The source code is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/jyh-learning/SSSNMF</uri> .
Correcting PAR Data from Photovoltaic Quantum Sensors on Remote Weather Stations on the Great Barrier Reef

Abstract The study addresses a network of remote weather stations on the Great Barrier Reef (GBR) that house Licor192 quantum sensors measuring photosynthetically active radiation (PAR) above water. There is evidence of significant degradation in the signal from the sensors after a 2-yr deployment. Main sources of uncertainty in the calibration are outlined, which include degradation of the photodiode, soiling of the sensors by dust and salt spray, cosine responses, and sensitivity to air temperature. Raw PAR data are improved using correction factors based on a cloudless PAR model. Uncertainties in cosine responses of the instrument are low but significant errors may occur if the supporting platform is misaligned and not horizontal. A set of recommendations are provided to improve the quality of the PAR data. Significance Statement A method is described to correct historical PAR data collected on the Great Barrier Reef, such that these valuable observations may be improved and used effectively.
A visual-degradation-inspired model with HSV color-encoding for contour detection

Given energy metabolism, visual information degradation plays an essential role in the retina- lateral geniculate nucleus (LGN)-primary visual cortex (V1)-secondary visual cortex (V2) pathway, and is a pivotal issue for visual information processing. Degradation helps the visual nervous system conserve brain energy and efficiently perceive the real world even though a small fraction of visual information reaches the early visual areas. The coding of contour features (edge and corner) is achieved in the retina-LGN-V1-V2 pathway. Based on the above, we proposed a contour detection model based on degradation (CDMD).Inspired by pupillary light reflex regulation, we took into consideration the novel approach of the hue-saturation-value (HSV) module for color encoding to meet the subtle chromaticity change rather than using the traditional red-green-blue (RGB) module, following the mechanisms of dark (DA) and light (LA) adaptation processes in photoreceptors. Meanwhile, the degradation mechanism was introduced as a novel strategy focusing only on the essential information to detect contour features, mimicking contour detection by visual perception under the restriction of axons in each optic nerve biologically. Ultimately, we employed the feedback mechanism achieving the optimal HSV value for each pixel of the experimental datasets.We used the publicly available Berkeley Segmentation Data Set 500 (BSDS500) to assess the effectiveness of our CDMD model, introduced the F-measure to evaluate the results. The F-measure score was 0.65, achieved by our model. Moreover, CDMD with HSV has a better sensitivity for subtle chromaticity changes than CDMD with RGB.Experimental results demonstrated that our CDMD model, which functions close to the real visual system, achieved a more competitive performance with low computational cost than some state-of-the-art non-deep-learning and biologically inspired models. Compared with deep-learning-based algorithms, our model contains fewer parameters and computation time, does not require additional visual features, as well as an extra training process.Our proposed CDMD model is a novel approach for contour detection, which mimics the cognitive function of contour detection in early visual areas, and realizes a competitive performance in image processing. It contributes to bridging the gap between the biological visual system and computer vision.
Motion Estimation and Coding Structure for Inter-Prediction of LiDAR Point Cloud Geometry

In this paper, we investigate two fundamental problems of inter-prediction for Light Detection and Ranging (LiDAR) point cloud geometries: motion estimation (ME) and coding structure under the inter-exploration model of geometry-based point cloud compression (G-PCC). Under the inter-exploration model of G-PCC, the key to a good ME algorithm is to design an accurate criterion for estimating the bit cost of an octree node. In the previous work, a logarithmic relationship between the prediction distortion and the bit cost was used as the criterion. We first note that the multiscale binary prediction residue, instead of the prediction distortion, is the key factor in determining the bit cost. Then, a linear relationship between the number of 1s and 0s in the multiscale binary prediction residue and the bit cost is built and used as the ME criterion. In terms of the coding structure, only the IPPP coding structure is investigated in all previous geometry inter-prediction algorithms. The use of the hierarchical coding structure is first investigated in this paper. We further propose determining the use of the IPPP or hierarchical coding structure at the group of pictures (GoP)-level based on rate distortion optimization to improve the performance. The proposed algorithms are implemented in the inter-exploration model of G-PCC. The experimental results show that compared with the inter-exploration model of G-PCC, the proposed algorithms can provide an average of 2.1% bitrate savings.
Golden sine cosine SALP swarm algorithm for shape matching using atomic potential function

Abstract Salp swarm algorithm (SSA) is one of the efficient recent meta‐heuristic optimization algorithms, where it has been successfully utilized in a wide range of optimization problems in different fields. In the research process, it is found that it is very difficult to maintain the balance between the exploration and exploitation capabilities of a certain algorithm. Therefore, one of the main purposes of this article is to provide an algorithm that can intelligently balance between exploration and exploitation, so that it can balance exploration and exploitation capabilities. Later, in the research process, it was found that the sine and cosine function and the salp foraging trajectory have a high mathematical similarity, which greatly improves the optimization ability of the algorithm. In addition, the variable neighbourhood strategy can appropriately expand the optimization range of the algorithm. So in this paper, a novel golden sine cosine salp swarm algorithm with variable neighbourhood search scheme (GSCSSA‐VNS) is proposed, the another objective of proposing this algorithm is as a new optimization method for shape matching. As a relatively new branch, atomic potential matching (APM) model is inspired by potential field attractions. Compared to the conventional edge potential function (EPF) model, APM has been verified to be less sensitive to intricate backgrounds in the test image and far more cost effective in the computation process. Experimental results of four realistic examples show that GSCSSA‐VNS is able to provide very competitive results and outperforms the other algorithms.