Reviewerly/exHarmony huggingface dataset:
iPrivacy: LWE Enhanced image protection over cloud storage

Outsourcing image data to the cloud is the major challenge of cloud security that must be addressed, especially for maintaining privacy. Image consumes more space while compared to text. Hence the traditional techniques like AES, DES, RSA are well fitted to text data rather than image. The leakage of sensitive image is increased while outsourcing. In this paper, we design the framework for a novel light weight encryption(LWE) to outsource the encrypted image to the cloud without leaking the sensitive information on the cloud server and maintains the privacy. It also maintains the balance between security and computational overhead in client system to enhance privacy. Images cannot retrieve even by the untrusted cloud vendors. Extensive experiments are conducted to prove that the proposed work consumes execution time with standard AES to provide superior performance of client system. The proposed algorithm consumes energy more than 50% with standard AES. The proposed LWE work has demonstrated the encryption/decryption of collecting various images in the experimental results and performance analysis to satisfactory security.
Swarm-Inspired Task Scheduling Strategy in Cloud Computing

Cloud computing is the most emerging technology which provides sharing of computing resources and data storage through virtualization concept. However, managing plenty of virtualized resources made scheduling a difficult task in cloud computing. Task scheduling must be done in such a way that it must satisfy customer requirements and maintain the quality of service (QoS). In this paper, we proposed a method for resource allocation based on particle swarm optimization (PSO) algorithm and with two objectives which produce optimal task scheduling. The first objective is related to virtual machine processing, and the second objective is related to the time elapsed to complete the given task. Based on the throughput of these objectives, the virtual machines are allotted to the resources.
Navigational Control Analysis of Mobile Robot in Cluttered Unknown Environment Using Novel Neural-GSA Technique

A unique hybridized neural-GSA artificial intelligence strategy has been proposed in this current paper for the steerage of a wheeled mobile robot in an obstacle prone environment. In this work, a seven-layered back propagation neural network has been hybridized with GSA to synthesize a controller for the wheeled mobile robot. The inputs to the neural-GSA approach are front obstacle distance, left obstacle distance, right obstacle distance and target angle. The output from the neural network is intermediate steering angle. The inputs to the GSA system in neural-GSA technique are front obstacle distance, left obstacle distance, right obstacle distance and intermediate steering angle. The output from the GSA controller is final steering angle. During the research, several simulations are carried out. Using the proposed neural-GSA strategy as well as theoretical results, it has been found out that the robot can successfully navigate in an obstacle prone environment.
Interpretable artificial intelligence: Closing the adoption gap in healthcare

Healthcare is poised to enter a new era of intelligent systems and enhanced human connection. The potential of artificial intelligence (AI) and machine learning (ML) technologies to assist decisions, optimize operations, and to free up quality human time will revolutionize how humans deliver and receive care. The success and expert-level performance of AI-based diagnostic systems have ushered in unprecedented optimism. However, there is a growing concern, about the ethics, safety, and equity in the delivery of care. The lack of clarity about its workings and the subsequent distrust has adversely affected the relationship of AI with the caregivers and receivers, thus precluding adoption. In this chapter we discuss interpretable AI as a road map toward building trusted AI that gets adopted in healthcare. The emphasis on the accuracy of AI alone needs to be revisited. Useful models will not only be accurate, but will also fulfill auxiliary criteria such as safety, fairness, privacy, accountability, and transparency. We will discuss why interpretability is key to adoption, what are its evolving meanings, and how it is realized across different classes of AI. We also compare some of these methods through concrete examples and a road map for the future for building toward the goal of trusted AI in healthcare, one paradigm at a time.
Image Identification of Grayscale-Based JPEG Images for Privacy-Preserving Photo Sharing Services

We propose an image identification scheme for double-compressed JPEG images encrypted by a grayscale-based encryption method, where the encrypted JPEG images are referred to as grayscale-based JPEG images, that has been proposed for Encryption-then-Compression (EtC) systems with JPEG compression. The proposed scheme aims to identify encrypted JPEG images that are generated from an original JPEG image. To store images without any visual sensitive information on photo sharing services, grayscale-based JPEG images are generated by using the grayscale-based encryption method. The use of the grayscale-based JPEG images and feature vectors extracted from the JPEG images allows us to identify images not only recompressed multiple times but re-encrypted with different keys. In an experiment, the proposed scheme is shown to have a high identification performance, even when images are recompressed multiple times and re-encrypted with different keys.
Tracking of Multiple Targets Across Distributed Platforms with FOV Constraints

This paper considers the scenario of tracking multiple moving targets across spatially distributed sensors with limited dynamic range. The targets considered are indistinguishable based on signal return or appearance, therefore, utilizing all available information across operating sensors is paramount for reducing target distribution uncertainty. Previous work has used the FOV limitations to utilize absent detection's of non responding sensors to improve posterior probability estimation. For the measurement to target assignment problem, the Joint Probabilistic Data Association particle filter is used for tracking targets from several aerial platforms with nadir cameras. The estimator continuously updates target position states while obtaining direct measurement of the targets or non-observations. In addition to modeling dynamic range constraints, it is also assumed there is a known probability of detection which will be implemented in addition to the field of view limitations to derive the likelihood function for the particle filter. It is shown that non-observations of the target improves the posterior target distribution, lowering the uncertainty when the target cannot be directly observed. In addition to presenting a filter that performs data association and tracking, a track-health monitoring scheme is proposed that monitors system performance.
An Efficient Symmetric Key-Based Lightweight Fully Homomorphic Encryption Scheme

Maintaining the secrecy and privacy of data, generally in cloud scenario, has become an intense challenge for the present day’s practical applications. However, transferring private data to any third party consists of large amount of risks of disclosure of private data while computation. This problem can be addressed by performing computations on encrypted data without decrypting it. This utility is evolved as fully homomorphic encryption, which is a great way for securing data which can be manipulated by an untrusted server. In this paper, we have given a fully homomorphic encryption protocol which is lightweight in nature and utilizing symmetric key. Analysis of the scheme confirms that our proposed system is efficient and practical to adopt it in various cloud computation applications.
Vehicle Pose Estimation Based on Object Contour

Object recognition and pose estimation are the most fundamental computer vision problems. The pose estimation is highly essential in autonomous driving. In this article, we will look into a method for assessing the position of vehicles based on the external contour of an object. Once the features have been defined, vehicle pose estimation can be considered as a problem of measuring the similarity between the features of the obtained image and the reference. In this work, the external contours of CAD models of vehicles at different viewing angles are taken as a reference. The test results, presented to illustrate the proposed approach, confirm the relevance of this method.
A Behavior-Based Population Tracker Can Parse Aggregate Measurements to Differentiate Agents

Closed-loop state estimators that track the movements and behaviors of large-scale populations have significant potential to benefit emergency teams during the critical early stages of disaster response. Such population trackers could enable insight about the population even where few direct measurements are available. In concept, a population tracker might be realized using a Bayesian estimation framework to fuse agent-based models of human movement and behavior with sparse sensing, such as a small set of cameras providing population counts at specific locations. We describe a simple proof-of-concept for such an estimator by applying a particle-filter to synthetic sensor data generated from a small simulated environment. An interesting result is that behavioral models embedded in the particle filter make it possible to distinguish among simulated agents, even when the only available sensor data are aggregate population counts at specific locations.
Video Dynamic Target Processing Method Based on FPGA

As the "eyes" of the city, video surveillance technology is getting more attention. For many complex scenes in video surveillance, such as criminal detectives, illegal vehicle identification, mall anti-theft, etc., detecting moving targets has become a demand of people. In response to such a situation, this design uses an FPGA development board with ZYNQ-7000 SoC to design a detection and processing method for moving targets. The design uses the camera to capture video images and store the data in multiple VDMAs, and then sends them into the image detection module, extracts the contour of the moving target by the frame difference method, and finally stores the result for data output display and analog alarm. The IP core of the detection algorithm is realized by Vivado HLS design in computer. The two-frame difference method and the three-frame difference method are improved by adding optimization instructions and image processing methods. In the same video, the detection results of these two methods are more intuitive than the background subtraction method. At the same time, they are not affected by the surrounding environment and are not sensitive to illumination. The system finally realizes the detection speed and clear contour extraction effect in real time, and can be applied to the field of video surveillance.
Tree Based Search Algorithm for Binary Image Compression

The demand for large-scale image data grows increasingly fast, resulting in a need for efficient image compression. This work aims at improving the lossless compression of binary images. To this end, we propose the use of a tree-based optimization algorithm, which searches for the best partitions of the input image into non-uniform blocks, and for the best combination of scan directions, with the goal of achieving more efficient compression of the resulting sequences of intervals between successive symbols of the same kind. The tree-based search algorithm searches for the best grid structure for adaptively partitioning the image into blocks of varying sizes. Extensive simulations of this search algorithm on various datasets demonstrated that we can achieve significantly higher compression on average than various standard binary image compression methods such as the JPEG 2000 and JBIG2 methods.
A Comparative Study of Opponent Type Effects on Speed of Learning for an Adversarial Q-Learning Agent

One goal of artificial intelligence has been to approximate human thought. The ability to learn is common to both humans and artificial intelligence agents, but the methods can sometimes differ. Reinforcement learning is one area where machine learning was initially based on the learning methods of humans and animals. In the case of two-player, turn-based adversarial games, it is advantageous to be able to train the learning agent against an opponent. The skill level of the opponent, however, is important, and can lead to different learning speeds, breadth of learning, or efficiency. We demonstrate the outcomes when having a learning agent play a simple game (tic-tac-toe) against an unskilled opponent, an opponent of equal skill, and an advanced opponent.
Expanding Kinodynamic Optimization Solutions with Recurrent Neural Networks and Path-tracking Control

This paper explores a methodology for training recurrent neural networks in replicating path planning solutions from optimization problems. Training data is generated from a kinodynamic rapidly-exploring random tree, from which a recurrent neural network is trained upon to produce the state path through fixed time-step execution. Path-tracking controllers are formulated to follow the path generated by the network alongside the use of local potential functions to mitigate minor constraint violations. The control signal from such a controller should mimic that of the optimized solution. Preliminary results for a 2D dynamics problem with obstacle constraints showcase the ability of this approach to achieve the desired controller execution and resulting state path. We also show that better network training and greater amounts of data lead to an increase in the overall performance.
A Model to Implement and Secure Online Documentation using Blockchain

The past decade has seen a tremendous rise in the use of blockchains for facilitating a trust based decentralized financial framework, in hope of eliminating dependency on core institutions such as banks. A blockchain stores a chronological record for a document detailing all the alterations that have been made to it since its inception. This data contained within this document may be anything, e.g. a lease document, personal files, etc. Smart documents can be used everywhere within the industry or an enterprise. Although blockchain based structures are much less eco-friendly in comparison to the ones with a database-based approach. Inter Planetary File System can be used to remotely store massive quantities of statistical data that may be involved in a blockchain transaction and address it in the actual chain by an absolute, everlasting link. Each report and all the blocks in a chain are assigned a unique fingerprint known as a cryptographic hash. This virtual cryptographic signature is used to verify legitimacy of each block. In this paper with the help of IPFS, along with other encryption methods we propose a secure and tamper proof model for blockchain based financial transactions.
The digital age and its discontents

Three decades into the ‘digital age’, the promises of emancipation of the digital ‘revolution’ in education are still unfulfilled. Furthermore, digitalization seems to generate new and unexpected challenges – for example, the unwarranted influence of digital monopolies, the radicalization of political communication, and the facilitation of mass surveillance, to name a few. This volume is a study of the downsides of digitalization and the re-organization of the social world that seems to be associated with it. In a critical perspective, technological development is not a natural but a social process: not autonomous from but very much dependent upon the interplay of forces and institutions in society. While influential forces seek to establish the idea that the practices of formal education should conform to technological change, here we support the view that education can challenge the capitalist appropriation of digital technology and, therefore, the nature and direction of change associated with it. This volume offers its readers intellectual prerequisites for critical engagement. It addresses themes such as Facebook’s response to its democratic discontents, the pedagogical implications of algorithmic knowledge and quantified self, as well as the impact of digitalization on academic profession. Finally, the book offers some elements to develop a vision of the role of education: what should be done in education to address the concerns that new communication technologies seem to pose more risks than opportunities for freedom and democracy. Matteo Stocchetti (PhD) is Senior Lecturer at Arcada University of Applied Science and Docent in Political Communication at University of Helsinki and Abo Akademi University. The contributors of the volume include international experts on critical approaches to pedagogy, education and technology.
An Architecture Model for Active Cyber Attacks on Intelligence Info-communication Systems:Application Based on Advance System Encryption (AES-512) Using Pre-Encrypted Search Table and Pseudo-Random Functions(PRFs)

Recently, obtaining security in info-communication systems becoming the most globally used method of data security for communications through unprotected channels. Although however, cyber-security is one of the most critical economic and national security challenges. Life has become more subjected on Internet-based tools, and there is a growing demand for a cyber-security centre to gain safe structure, and cyber-space development. Information is a principal element in the info-communication system, where trusted service providers have a security system in place to prevent, support and protect their data against malicious attack or theft of information. To encrypt data with the larger size, mostly symmetric key encryption is using like advanced encryption standard (AES). This paper, is dealing first, with the characteristics of data that can be addressed to the cyber attacks. Secondly, a formal definition model is applied to improve the performance of the application where session keys are used to communicate using a pre-encrypted search table approach. The approach is suitable for programs that communicate in session mode and the key does not change frequently.
Hanoman adventure game based on Android

Abstract The use of smartphones to play mobile games is increasingly being used by everyone from children to adults. Along with the rapid development of the game industry, game engine software is now popping up, one of which is Construct 2. Construct 2 is an HTML5-based game maker software that is devoted to the 2D platform. Games can be a medium for cultural preservation in a country. One of them is Indonesian culture that needs to be preserved is wayang culture. This adventure game will raise a cultural puppet character named Hanoman, Hanoman is a figure in the form of a white monkey who acts as a character in helping Rama to save Shinta. This Hanoman adventure game in carrying out missions on each level will face the enemy characters who must be defeated to be able to open the next level. In this game, there are 3 levels where each level has different difficulties so that it can provide challenges for its users.
Performance Evaluation of ASR for Isolated Words in Sindhi Language

Speech Recognition is an advancing technology with vast amounts of actual application and focal points. Still existing work majorly spins around dialects like English and Hindi, though work has nowadays begun for different foreign languages. Since the significant languages that are centered around are unknown to most of the masses, it has created a language and tech availability barrier. Therefore, a large portion of masses ends up being deprived of reaping the benefits of Speech Processing. This paper presents an initiative to overcome one of the limitations of Speech Recognition, which is the language barrier by creating a framework for Automatic Speech Recognition (ASR) of isolated words in Sindhi language and displays it in English script. The Feature Extraction technique used is Mel Frequency Cepstral Coefficients (MFCC), and Hidden Markov Model (HMM) for recognition purpose. Two models within HMM are chosen for performance evaluation, i.e., Gaussian HMM and Gaussian Mixture Model (GMM). Of the two, Gaussian HMM performed much better, giving an accuracy of 72.14%.
Hindsight Reward Shaping in Deep Reinforcement Learning

Recent developments in the field of deep reinforcement learning (DRL) have shown that reinforcement learning (RL) techniques are able to solve highly complex problems by learning an optimal policy for autonomous control tasks. Although RL shows great promise in sequential decision-making problems in dynamic environments, there are still caveats associated with the framework. One such pitfall is the time taken to converge due to sparse and delayed rewards, known as the temporal credit assignment problem in RL. This paper addresses the problem by introducing a simple yet effective method of distributing the discounted terminal state reward backwards in time for episodic environments after an episode has reached terminal state. The shaped reward transitions are then added to the experience replay buffer.
Perturbed Iterate SGD for Lipschitz Continuous Loss Functions

This paper presents an extension of stochastic gradient descent for the minimization of Lipschitz continuous loss functions. Our motivation is for use in non-smooth non-convex stochastic optimization problems, which are frequently encountered in applications such as machine learning. Using the Clarke $$\epsilon $$ -subdifferential, we prove the non-asymptotic convergence to an approximate stationary point in expectation for the proposed method. From this result, a method with non-asymptotic convergence with high probability, as well as a method with asymptotic convergence to a Clarke stationary point almost surely are developed. Our results hold under the assumption that the stochastic loss function is a Carathéodory function which is almost everywhere Lipschitz continuous in the decision variables. To the best of our knowledge, this is the first non-asymptotic convergence analysis under these minimal assumptions.
Distributed Resampling Gaussian Particle Filtering for Heterogeneous Networks

In this work, we propose a fully distributed resampling Gaussian particle filtering (D-ReGPF) algorithm for heterogeneous networks, where each sensor could implement distinct adaptation rules. In order to enhance the robustness of the algorithm, we further develop an adaptive combination strategy. Illustrative simulations of the direct target tracking problem involving heterogeneous networks validate that the proposed D-ReGPF incorporated with the adaptive combiners could markedly outperform the existing distributed Gaussian particle filtering with conventional combiners for homogeneous networks.
Effect of teaching material based on mobile learning to learning outcomes of natural environment

Abstract Learning materials that are integrated with technology are currently being used. This study aims to determine the effect of mobile learning-based teaching materials on student learning outcomes in thematic learning in elementary schools. This study uses an experimental method with a 2x2 level treatment design, to answer the question of how the influence of mobile learning-based teaching materials for elementary schools. The results showed that there were significant changes to the learning outcomes of students after using mobile learning-based teaching materials, compared to using conventional teaching materials. Thus it can be concluded, that teaching materials based on mobile learning can affect the learning outcomes of elementary school students.