Reviewerly/exHarmony huggingface dataset:
QSRNet: Estimating Qualitative Spatial Representations from RGB-D Images

Humans perceive and describe their surroundings with qualitative statements (e.g., "Alice's hand is in contact with a bottle."), rather than quantitative values (e.g., 6-D poses of Alice's hand and a bottle). Qualitative spatial representation (QSR) is a framework that represents the spatial information of objects in a qualitative manner. Region connection calculus (RCC), qualitative trajectory calculus (QTC), and qualitative distance calculus (QDC) are some popular QSR calculi. With the recent development of computer vision, it is important to compute QSR calculi from the visual inputs (e.g., RGB-D images). In fact, many QSR application domains (e.g., human activity recognition (HAR) in robotics) involve visual inputs. We propose a qualitative spatial representation network (QSRNet) that computes the three QSR calculi (i.e., RCC, QTC, and QDC) from the RGB-D images. QSRNet has the following novel contributions. First, QSRNet models the dependencies among the three QSR calculi. We introduce the dependencies as kinematics for QSR because they are analogous to the kinematics in classical mechanics. Second, QSRNet applies the 3-D point cloud instance segmentation to compute the QSR calculi. The experimental results show that QSRNet improves the accuracy in comparison to the other state-of-the-art techniques.
Dense Broad Learning System with Proportional Integral Differential and Adaptive Moment Estimation

Deep learning suffers from many notorious issues such as low convergence rate, over-fitting, and time-consuming. To alleviate these problems, an alternative learning framework with a non-iterative training mechanism named Broad Learning System (BLS) was proposed, which randomly assigns the parameters of hidden nodes and frozen them throughout the training process and then obtains its output weights using the ridge regression theory. This training method makes BLS have very high training efficiency. However, using ridge regression to solve the output weights cannot guarantee the stability of the solution in many cases, especially when the number of training samples is large, which may cause over-fitting and instability of BLS models. To solve this problem, we propose an improved BLS with a dense architecture and use the Proportional-Integral-Differential (PID) and Adaptive moment estimation (Adam) to replace the ridge regression operation. The new algorithm is called PID-A-DBLS, and its advantages include: 1) dense architecture can improve the feature extraction ability of the model; 2) using PID and Adam to solve the output weights can avoid the disadvantages of ridge regression. Extensive experimental results on four benchmark data sets show that PID-A-DBLS can achieve much better generalization ability and stability than BLS and its variants.
Dynamic Path Finding using Ant Colony Optimization

Ant Colony Optimization (ACO) has been commonly applied in solving discrete optimization problems. This is an attempt to apply ACO in a dynamic environment for finding the optimal route. To create a dynamically changing scenario, in addition to distance, constraints such as air quality, congestion, user feedback, etc are also incorporated for deciding the optimal route. Max-Min Ant System (MMAS), an ACO algorithm is used to find the optimal path in this dynamic scenario. A local search parameter ε is also introduced in addition to ρ to improve the exploration of unused paths. Adaptability was studied by dynamically changing the costs associated with different parameters.
Factorization statistics of restricted polynomial specializations over large finite fields

For a polynomial F(t, A1, …, An) ∈ $$\mathbb{F}$$ p[t, A1, …, An] (p being a prime number) we study the factorization statistics of its specializations $$F\left({t,{a_1}, \ldots ,{a_n}} \right) \in {\mathbb{F}_p}\left[t \right]$$ with (a1, …, an) ∈ S, where $$S \subset \mathbb{F}_p^n$$ is a subset, in the limit p → ∞ and deg F fixed. We show that for a sufficiently large and regular subset $$S \subset \mathbb{F}_p^n$$ , e.g., a product of n intervals of length H1, …, Hn with $$\prod\nolimits_{i = 1}^n {{H_n} > {p^{n - 1/2 +\epsilon}}} $$ , the factorization statistics is the same as for unrestricted specializations (i.e., $$S = \mathbb{F}_p^n$$ ) up to a small error. This is a generalization of the well-known Pólya-Vinogradov estimate of the number of quadratic residues modulo p in an interval.
Introduction to Evolutionary Data Clustering and Its Applications

Clustering is concerned with splitting a dataset into groups (clusters) that represent the natural homogeneous characteristics of the data. Remarkably, clustering has a crucial role in numerous types of applications. Essentially, the applications include social sciences, biological and medical applications, information retrieval and web search algorithms, pattern recognition, image processing, machine learning, and data mining. Even that clustering is ubiquitous over a variety of areas. However, clustering approaches suffer from several drawbacks. Mainly, they are highly susceptible to clusters' initial centroids which allows a particular dataset to easily fall within a local optimum. Handling clustering as an optimization problem is deemed an NP-hard optimization problem. However, metaheuristic algorithms are a dominant class of algorithms for solving tough and NP-hard optimization problems. This chapter anticipates the use of evolutionary algorithms for addressing the problem of clustering optimization. Therefore, it presents an introduction to clustering and evolutionary data clustering, reviews thoroughly the applications of evolutionary data clustering and its implementation approaches.
A Practical Black-Box Attack Against Autonomous Speech Recognition Model

With the wild applications of machine learning (ML) technology, automatic speech recognition (ASR) has made great progress in recent years. Despite its great potential, there are various evasion attacks of ML-based ASR, which could affect the security of applications built upon ASR. Up to now, most studies focus on white-box attacks in ASR, and there is almost no attention paid to black-box attacks where attackers can only query the target model to get output labels rather than probability vectors in audio domain. In this paper, we propose an evasion attack against ASR in the above-mentioned situation, which is more feasible in realistic scenarios. Specifically, we first train a substitute model by using data augmentation, which ensures that we have enough samples to train with a small number of times to query the target model. Then, based on the substitute model, we apply Differential Evolution (DE) algorithm to craft adversarial examples and implement black-box attack against ASR models from the Speech Commands dataset. Extensive experiments are conducted, and the results illustrate that our approach achieves untargeted attacks with over 70% success rate while still maintaining the authenticity of the original data well.
Secure Data Validation and Transmission in Cloud and IoT Through Ban Logic and KP-ABE

Background: Cloud computing is a service that is being accelerating its growth in the field of information technology in recent years. Privacy and security are challenging issues for cloud users and providers. Obective: This work aims at ensuring secured validation of user and protects data during transmission for users in a public IoT-cloud environment. Existing security measures however fails by their single level of security, adaptability for large amount of data and reliability. Therefore, to overcome these issues and to achieve a better solution for vulnerable data. Method: The suggested method utilizes a secure transmission in cloud using key policy attribute based encryption (KPABE). Initially, user authentication is verified. Then the user data is encrypted with the help of KP-ABE algorithm. Finally, data validation and privacy preservation are done by Burrows-Abadi-Needham (BAN) logic. This verified, and shows that the proposed encryption is correct, secure and efficient to prevent unauthorized access and prevention of data leakage so that less chances of data/identity, theft of a user is the analysis and performed by KP-ABE, that is access control approach. Results: Here the method attains the maximum of 88.35% of validation accuracy with a minimum 8.78ms encryption time, which is better when, compared to the existing methods. The proposed mechanism is done by MATLAB. The performance of the implemented method is calculated based on the time of encryption and decryption, execution time and validation accuracy. Conclusion: Thus the proposed approach attains the high IoT-cloud data security and increases the speed for validation and transmission with high accuracy and used for cyber data science processing.
Graphfool: Targeted Label Adversarial Attack on Graph Embedding

Graph embedding learns low-dimensional representations for nodes or edges on the graph, which is widely applied in many real-world applications. Excessive graph mining promotes the research of attack methods on graph embedding. Most attack methods generate perturbations that maximize the deviation of the prediction confidence. They are difficult to accurately misclassify the instances into the target label, and the nonminimized perturbations are more easily detected by defense methods. To address these problems, we propose Graphfool, a novel targeted label adversarial attack on graph embedding. It can generate adversarial graphs to attack graph embedding methods via classification boundary and gradient information in the target graph embedding method. Graphfool first estimates the classification boundaries of different categories. Then, it calculates the minimum perturbation matrix to misclassify the attacked node according to the target classification boundary. Finally, the adjacency matrix is modified according to the maximum absolute value of the perturbation matrix. Extensive experiments demonstrate that Graphfool achieves the state-of-the-art attack performance with minimum perturbations. Besides, the possible defense experiments further prove that the perturbations generated by Graphfool are more imperceptible.
PRIORITY MERGE AND INTERSECTION MODALITIES

Abstract We study the logic of so-called lexicographic or priority merge for multi-agent plausibility models. We start with a systematic comparison between the logical behavior of priority merge and the more standard notion of pooling through intersection, used to define, for instance, distributed knowledge. We then provide a sound and complete axiomatization of the logic of priority merge, as well as a proof theory in labeled sequents that admits cut. We finally study Moorean phenomena and define a dynamic resolution operator for priority merge for which we also provide a complete set of reduction axioms.
Study of Clarification Android Based Worksheet of Topic Cartesian Coordinate at Level Junior Secondary

Abstract This study aims to design an Android-based student worksheet on the topic of cartesian coordinate learning and obtain evidence for clarification of research on prototype design. The design research method was carried out based on stages, namely research clarification, descriptive study 1, prescriptive study, and descriptive study II. This research revealed the results of the research clarification stage by involving 35 Mathematics and Natural Sciences students of FMIPA UNM. They were asked to test the prototype and provide responses through the questionnaire provided. There are three main things to be responded by respondents, namely the suitability of the format, the material submitted, and worksheet form. The data obtained were analysed descriptively quantitatively. The results show that the prototype format meets the suitability. Likewise, the criteria for the material presented and the prototype designed. In other words, this Android-based worksheet prototype meets the stated design objectives.
Philosophical Reference as Method of Artistic World Modelling in Prose of the “Generation of Forty-Year-Olds” (by the Material of A. Bitov’s and A. Kim’s Works)

The main purpose of the study is to determine specificity of functioning of double artistic reference as a method of world modelling in the process of ontologising cultural and philosophical images in prose of the “generation of forty-year-olds”. The article presents an analysis of the mythologeme “forest”, corresponding to the receptive concepts of “The Philosophy of the Common Task” by N. Fyodorov and the novella “Soul” by A. Platonov. Scientific novelty of the study lies in the fact that for the first time referential field of the context peculiar to the mythopoetic meta-image “forest” in works by A. Bitov and A. Kim was identified, its components being archetypical, cultural and philosophical meanings. As a result, the “world-like” meta-image “forest” is defined as a meaning-bearing concentric circle of providential premonition about downfall of the “common task” in social entropy of “existential loneliness”.
Quantized Reservoir Computing on Edge Devices for Communication Applications

With the advance of edge computing, a fast and efficient machine learning model running on edge devices is needed. In this paper, we propose a novel quantization approach that reduces the memory and compute demands on edge devices without losing much accuracy. Also, we explore its application in communication such as symbol detection in 5G systems, attack detection of smart grid, and dynamic spectrum access. Conventional neural networks such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) could be exploited on these applications and achieve state-of-the-art performance. However, conventional neural networks consume a large amount of computation and storage resources, and thus do not fit well to edge devices. Reservoir computing (RC), which is a framework for computation derived from RNN, consists of a fixed reservoir layer and a trained readout layer. The advantages of RC compared to traditional RNNs are faster learning and lower training costs. Besides, RC has faster inference speed with fewer parameters and resistance to overfitting issues. These merits make the RC system more suitable for applications running on edge devices. We apply the proposed quantization approach to RC systems and demonstrate the proposed quantized RC system on Xilinx Zynq®-7000 FPGA board. On the sequential MNIST dataset, the quantized RC system utilizes 62%, 65%, and 64% less of DSP, FF, and LUT, respectively compared to the floating-point RNN. The inference speed is improved by 17 times with an 8% accuracy drop.
Optimizing learning in associative networks and gamification: Lessons learnt and implications

Technological trends have evolved, extended and converged in the 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century. Much of these evolutions are incremental, differential and the aim is to sustain or rise above others amidst a dynamic landscape. As data and knowledge bases extend, the fitness of features in each evolution requires systemic modelling targeting multi-objectives. Identification of the local regions of interest and significant features enable more efficient optimization. The information core, current or new invariant features which proceed to the next iteration coupled with gamification in certain domains, can lead to varying implications. This paper briefly reviews literature on associative networks, case-based reasoning, combinations and permutations, optimization, and gamification and conclude with implications and opportunities for symbiotic networks.
An Insight into Tool and Software Used in AI, Machine Learning and Data Analytics

Machine learning is a fantastic development on the off chance that you use it correctly. We can use machine learning models to demonstrate the importance of predictions on various projects. Acing machine learning tools would let you play with the information, train your models, find new systems, and make your computations efficiently. The pace of AI is overgrowing, and it is getting more noteworthy than some other time in late memory to perceive what choices you have for AI tools, libraries, stages, and so forward. Moreover, the need for investigation of critical information has opened various avenues in machine learning. Various open-source tools can be explored for performing analysis tasks as they are more renowned, straightforward, and executed orchestrated than the paid version. Many open-source tools do not require any coding and make sense of passing on the best results compared to paid structures. In this book chapter, we discuss the role of machine learning analytics in artificial intelligence. We survey the various kinds of open-source tools available on the Internet to take up projects in this field. Besides elucidating the application domains in which these analytics tools can be used, we also validate the results using a case study performed to detect fraudulent transactions. The chapter reviews data analytics tools, libraries, and software, what they are typically used for, what points of interest, and inconveniences they offer to choose from them based on his convenience.
Quantum Crypto-Economics: Blockchain Prediction Markets for the Evolution of Quantum Technology

Two of the most important technological advancements currently underway are the advent of quantum technologies, and the transitioning of global financial systems towards cryptographic assets, notably blockchain-based cryptocurrencies and smart contracts. There is, however, an important interplay between the two, given that, in due course, quantum technology will have the ability to directly compromise the cryptographic foundations of blockchain. We explore this complex interplay by building financial models for quantum failure in various scenarios, including pricing quantum risk premiums. We call this ‘quantum crypto-economics’.
Optimizing CNN using Fast Fourier Transformation for Object Recognition

This paper proposes to use Fast Fourier Transformation-based U-Net (a refined "fully convolutional networks") and perform image convolution in neural networks. Leveraging the Fast Fourier Transformation, it reduces the image convolution costs involved in the Convolutional Neural Networks (CNNs) and thus reduces the overall computational costs. The proposed model identifies the object information from the images. We apply the Fast Fourier transform algorithm on an image data set to obtain more accessible information about the image data, before segmenting them through the U-Net architecture. More specifically, we implement the FFT-based convolutional neural network to improve the training time of the network. The proposed approach was applied to publicly available Broad Bioimage Benchmark Collection (BBBC) dataset. Our model demonstrated improvement in training time during convolution from 600 - 700 ms/step to 400-500 ms/step. We evaluated the accuracy of our model using Intersection over Union (IoU) metric showing significant improvements.
Automatic Metadata Extraction From Iranian Theses And Dissertations

Metadata extraction is a critical part of scholarly information management. This paper outlines an approach to extract metadata information from Persian thesis documents. The proposed method comprises of two subtasks: In the initial stage of the process, the boundary of the thesis's front matter and the body is detected automatically and the main body is discarded. Once Body detection has been conducted, the remaining paragraphs are classified into desired metadata. The classification tasks are carried out using Support Vector Machine technique. In an experiment with thesis documents, novel features are introduced, compatible with Iranian thesis/dissertation documents. The findings confirm the suggested method achieves 91.36% F1-score.
Dynamic Array Double-Access Attack in Ethereum

We propose a new type of variable coverage method called Dynamic Array Double-Access Attack (DADA Attack) in Ethereum Virtual Machine (EVM). Such attack can read and write operations anywhere in the storage area, resulting in abnormal execution of contract and out of control. Vulnerable contracts with such attacks will directly lead to the loss of a large amount of property. To evaluate the effectiveness of such Double-Access Attack, we conducted theoretical analysis and experimental verification, finding attack success rate is 100% under the condition that dynamic array length is controllable.
An Exploration-driven Reinforcement Learning Model for Video Streaming Scheduling in 5G-Powered Drone

Abstract In the process of wireless network video streaming, especially in more complex scenarios (such as video transmission of 5G-powered drones), analyzing the quality of experience (QoE) of the video streaming is a very crucial task. Thus attention should be paid to the dynamic interaction between QoE indicators including buffer starvation probability and traffic load. This paper proposes a video streaming scheduling model based on reinforcement learning. By learning the correlation between user behavior and traffic patterns, a series of resource allocation strategies that optimize QoE indicators are obtained. Since there is a certain degree of randomness in the network status at each moment in the transmission process, the model introduces exploration rewards to solve the noise problem of random environments. At the same time, this mechanism enables the model to fully explore the environment even when the reward is sparse, so as to obtain an effective scheduling strategy. Simulation experiments have proved that our model can improve the long-term QoE of video streaming in different network environments.
Adaptive Momentum Coefficient for Neural Network Optimization

We propose a novel and efficient momentum-based first-order algorithm for optimizing neural networks which uses an adaptive coefficient for the momentum term. Our algorithm, called Adaptive Momentum Coefficient (AMoC), utilizes the inner product of the gradient and the previous update to the parameters, to effectively control the amount of weight put on the momentum term based on the change of direction in the optimization path. The algorithm is easy to implement and its computational overhead over momentum methods is negligible. Extensive empirical results on both convex and neural network objectives show that AMoC performs well in practise and compares favourably with other first and second-order optimization algorithms. We also provide a convergence analysis and a convergence rate for AMoC, showing theoretical guarantees similar to those provided by other efficient first-order methods.
Analysis of Coordination Structures of Partially Observing Cooperative Agents by Multi-agent Deep Q-Learning

We compare the coordination structures of agents using different types of inputs for their deep Q-networks (DQNs) by having agents play a distributed task execution game. The efficiency and performance of many multi-agent systems can be significantly affected by the coordination structures formed by agents. One important factor that may affect these structures is the information provided to an agent’s DQN. In this study, we analyze the differences in coordination structures in an environment involving walls to obstruct visibility and movement. Additionally, we introduce a new DQN input, which performs better than past inputs in a dynamic setting. Experimental results show that agents with their absolute locations in their DQN input indicate a granular level of labor division in some settings, and that the consistency of the starting locations of agents significantly affects the coordination structures and performances of agents.