Reviewerly/exHarmony huggingface dataset:
AES Based Algorithm for Image Encryption and Decryption

Security in transmission storage of digital image has its important data and image communication. AES is a well-known cipher that has block cipher which has several advantages. AES algorithm used for text data and also suitable for image encryption and decryption to protects confidential image data from an unauthorized access. In this paper we are developing verilog code to implement 128 bits .AES for image encryption and decryption which is synthesized and simulated on FPGA family of Spartan-6 using Xilinx ISE 12.4 tool.
Efficient Gradient Updating Strategies with Adaptive Power Allocation for Federated Learning over Wireless Backhaul

In this paper, efficient gradient updating strategies are developed for the federated learning when distributed clients are connected to the server via a wireless backhaul link. Specifically, a common convolutional neural network (CNN) module is shared for all the distributed clients and it is trained through the federated learning over wireless backhaul connected to the main server. However, during the training phase, local gradients need to be transferred from multiple clients to the server over wireless backhaul link and can be distorted due to wireless channel fading. To overcome it, an efficient gradient updating method is proposed, in which the gradients are combined such that the effective SNR is maximized at the server. In addition, when the backhaul links for all clients have small channel gain simultaneously, the server may have severely distorted gradient vectors. Accordingly, we also propose a binary gradient updating strategy based on thresholding in which the round associated with all channels having small channel gains is excluded from federated learning. Because each client has limited transmission power, it is effective to allocate more power on the channel slots carrying specific important information, rather than allocating power equally to all channel resources (equivalently, slots). Accordingly, we also propose an adaptive power allocation method, in which each client allocates its transmit power proportionally to the magnitude of the gradient information. This is because, when training a deep learning model, the gradient elements with large values imply the large change of weight to decrease the loss function.
Privacy-Preserving Kernel Computation For Vertically Partitioned Data

In this paper, we propose a secure and privacy-preserving technique for computing dot-product kernels on vertically distributed data.Our proposal is based on secure multi-party computation which provides theoretical guarantees on both security and privacy.We also provide a practical application of the method by adapting a kernel-based collaborative filtering technique to the federated setting.An extensive experimental evaluation shows the effectiveness of the proposed approach.
Image Descriptions' Limitations for People with Visual Impairments

Image descriptions aim to transcribe the visual content and are essential for people who do not have eyesight. Such image descriptions are generated manually or by Artificial Intelligence (AI) models. Despite its relevance, the emergence of automatic image descriptions was not motivated by people with visual impairments; thus, they still cause dissatisfaction in this audience. This paper provides a snowballing review of the limitations of image descriptions for people with visual impairments. We encountered thirteen image description issues, including those analogous to the ethics, such as people's appearances, gender, race, and disability. We identified five reasons why sighted people do not write image descriptions for the content they share, exposing the necessity of accessibility campaigns to awareness people of the social importance of image descriptions. Moreover, we discussed recommendations found in the literature that may support automated tools and sighted people for writing high-quality image descriptions. We hope our results will highlight the social significance of image descriptions and encourage the community to pursue further interdisciplinary researches that could potentially minimize the issues pointed out in our study.
A Discrete Crow Search Algorithm for Mining Quantitative Association Rules

Association rules are the specific data mining methods aiming to discover explicit relations between the different attributes in a large dataset. However, in reality, several datasets may contain both numeric and categorical attributes. Recently, many meta-heuristic algorithms that mimic the nature are developed for solving continuous problems. This article proposes a new algorithm, DCSA-QAR, for mining quantitative association rules based on crow search algorithm (CSA). To accomplish this, new operators are defined to increase the ability to explore the searching space and ensure the transition from the continuous to the discrete version of CSA. Moreover, a new discretization algorithm is adopted for numerical attributes taking into account dependencies probably that exist between attributes. Finally, to evaluate the performance, DCSA-QAR is compared with particle swarm optimization and mono and multi-objective evolutionary approaches for mining association rules. The results obtained over real-world datasets show the outstanding performance of DCSA-QAR in terms of quality measures.
Demystifying Cryptocurrency Mining Attacks: A Semi-supervised Learning Approach Based on Digital Forensics and Dynamic Network Characteristics

Cryptocurrencies have emerged as a new form of digital money that has not escaped the eyes of cyber-attackers. Traditionally, they have been maliciously used as a medium of exchange for proceeds of crime in the cyber dark-market by cyber-criminals. However, cyber-criminals have devised an exploitative technique of directly acquiring cryptocurrencies from benign users' CPUs without their knowledge through a process called crypto mining. The presence of crypto mining activities in a network is often an indicator of compromise of illegal usage of network resources for crypto mining purposes. Crypto mining has had a financial toll on victims such as corporate networks and individual home users. This paper addresses the detection of crypto mining attacks in a generic network environment using dynamic network characteristics. It tackles an in-depth overview of crypto mining operational details and proposes a semi-supervised machine learning approach to detection using various crypto mining features derived from complex network characteristics. The results demonstrate that the integration of semi-supervised learning with complex network theory modeling is effective at detecting crypto mining activities in a network environment. Such an approach is helpful during security mitigation by network
I Know Your Keyboard Input

Recently, smart devices equipped with microphones have become increasingly popular in people's lives. However, when users type on a keyboard near devices with microphones, the acoustic signals generated by different keystrokes may leak the user's privacy. This paper proposes a robust side-channel attack scheme to infer keystrokes on the surrounding keyboard, leveraging the smart devices' microphones. To address the challenge of non-cooperative attacking environments, we propose an efficient scheme to estimate the relative position between the microphones and the keyboard, and extract two robust features from the acoustic signals to alleviate the impact of various victims and keyboards. As a result, we can realize the side-channel attack through acoustic signals, regardless of the exact location of microphones, the victims, and the type of keyboards. We implement the proposed scheme on the commercial smartphone and conduct extensive experiments to evaluate its performance. Experimental results show that the proposed scheme could achieve good performance in predicting keyboard input under various conditions. Overall, we can correctly identify 91.2% of keystrokes with 10-fold cross-validation. When predicting keystrokes from unknown victims, the attack can obtain a Top-5 accuracy of 91.52%. Furthermore, the Top-5 accuracy of predicting keystrokes can reach 72.25% when the victims and keyboards are both unknown. When predicting meaningful contents, we can obtain a Top-5 accuracy of 96.67% for the words entered by the victim.
Effectiveness of Mobile Game-based Education on Algorithm Thinking: Informatic Engineering Case

Informatic engineering is one of the majors that are in high demand by prospective new students in Indonesia, but most of the new students have not learned lessons about algorithms and programming yet, so there are so many unsatisfactory grades in algorithm and programming lessons. The application of mobile game-based education algorithm is to find out whether using the algorithm learning method applied in mobile game-based can be useful or help to understand for users who are just starting to learn algorithms and programming. The target users to test the benefits gained from this mobile-based game are high school students, first-semester students majoring in informatic engineering or all users interested in learning programming languages. The algorithm learning material in the mobile game-based used in this study learns about some essential functions of the algorithm, namely the sequence function, looping function and procedural function. For a proof-of-concept, we implemented this mobile game-based education algorithm in a game called "KeyBot", as a case study that we propose. The game "KeyBot" has been tested by a number of users, from the user survey, it was concluded that the method of making mobile game-based education on the algorithm thinking is going well enough.
$F_0$-Noise-Robust Glottal Source and Vocal Tract Analysis Based on ARX-LF Model

This paper proposes a robust automatic speech analysis method based on a source-filter model constructed of an Auto-Regressive eXogenous (ARX) model and the Liljencrants-Fant (LF) model. The proposed method estimates glottal source waveform and vocal tract shape parameters using an analysis-by-synthesis approach. Structurally, the first step is to initialize the glottal source parameters using the inverse filter method, and the second step is to simultaneously estimate the glottal source waveform and the vocal tract shape parameters using an analysis-by-synthesis approach with an iterative algorithm. The proposed method was verified on synthetic voices with different glottal noise (signal to noise ratio) from 0 dB to 50 dB and different fundamental frequency ( $F_0$ ) from 80 Hz to 320 Hz levels. The results show that the proposed method achieved a much higher estimation accuracy than that of the state-of-the-art inverse filtering methods on both different glottal noise and different $F_0$ levels.
Mitigating Deep learning Attacks Against Text Image CAPTCHA Using Arabic Scheme

Confidentiality and availability are the main concerns of website stakeholders, entailing service-security trade-offs. Many solutions seek to improve security. The most popular one is the completely automated public Turing test (CAPTCHA) security framework, which distinguishes between human and bot activity by asking the user to perform a certain task, such as solving a mathematical equation or retyping text written in an image. Many people believe that websites using CAPTCHAs are secure, but today’s attackers using innovative technology, such as deep learning, that can break them in milliseconds. Many solutions have been proposed to overcome this issue, mainly relying on increased CAPTCHA complexity or technical CAPTCHAs too difficult for normal users. In this case, many customers may go to websites that use simple CAPTCHAs, at the expense of websites with more complex ones. This paper proposes a robust and simple image-based CAPTCHA using an Arabic scheme and noise to make breaking this captcha very difficult, even for deep learning approaches. The proposed model is evaluated using two-factor, which focuses on the usability and the ability to beak the proposed CAPTCHA. The first test has been done via an empirical experiment involving 16 people from different sectors, and the second test has involved breaking the model using Convolutional Neural Network (CNN). The experimental results demonstrate the superiority of the proposed model in security and usability.
On the Human Role in Generative Art: A Case Study of AI-driven Live Coding

The evolution of philosophical views on art is interwoven with trajectories of accelerating technological amelioration. In the emergence of generative algorithms there is a need for making sense of modern technologies that step in a realm previously reserved for humans – creativity. This paper aims to understand the role of the human in generative art by demystifying implications of black-box generative algorithms and their applications for artistic purposes. First, we shortly canvas the current state of practice and research in generative art, especially music. Then, we introduce Anastatica, a part performance, part installation built using data-driven generative live coding. Finally, we discuss implications of AI in art through a case study of Anastatica’s development and performance. We trace the path from algorithms to intelligence, applying musical and computer science theory to a practical case of generating a live coding musical performance, while focusing on aesthetic, compositional, conceptual, and phenomenological implications.
Bayesian approaches to variable selection in mixture models with application to disease clustering

In biomedical research, cluster analysis is often performed to identify patient subgroups based on patients' characteristics or traits. In the model-based clustering for identifying patient subgroups, mixture models have played a fundamental role in modeling. While there is an increasing interest in using mixture modeling for identifying patient subgroups, little work has been done in selecting the predictors that are associated with the class assignment. In this study, we develop and compare two approaches to perform variable selection in the context of a mixture model to identify important predictors that are associated with the class assignment. These two approaches are the one-step approach and the stepwise approach. The former refers to an approach in which clustering and variable selection are performed simultaneously in one overall model, whereas the latter refers to an approach in which clustering and variable selection are performed in two sequential steps. We considered both shrinkage prior and spike-and-slab prior to select the importance of variables. Markov chain Monte Carlo algorithms are developed to estimate the posterior distribution of the model parameters. Practical applications and simulation studies are carried out to evaluate the clustering and variable selection performance of the proposed models.
The Covid-19 Chatbot Application Using A Natural Language Processing Approach

Cases exposed to the Covid-19 virus in Indonesia until June 2021 continue to experience a spike in increases, to handle it, various government policies continue to be rolled out and the public needs to be given correct, precise and fast information so that mutual awareness can be built to suppress cases exposed to COVID-19. With this background, this study aims to design and build a COVID-19 chatbot system based on artificial intelligence based on the Natural Language Processing algorithm. This chatbot is expected to be a place to ask questions about all things related to covid-19 so that it can become a personal assistant with two-way communication that can be accessed quickly for 24 hours. This chatbot system was built using the Python programming language, Node.js server and MariaDB as the database. As a client, this chatbot is integrated with the popular instant messaging application in Indonesia, namely WhatsApp. The data set used to train the chatbot was 369 question data and spread into 46 question tags. Testing the chatbot system using blackbox testing, and to test the expected output, the chatbot was tested using 350 testing data and the accuracy rate of the chatbot in answering reached 54%.
Fully Secure Unbounded Zero Inner Product Encryption with Short Ciphertexts and Keys

Inner product encryption (IPE) generates a secret key for a predicate vector and encrypts a message under an attribute vector such that recovery of the message from a ciphertext requires the vectors to satisfy a linear relation. In the case of zero IPE (ZIPE), the relation holds if the inner product between the predicate and attribute vectors is zero. Over the years, several ZIPE schemes have been proposed with numerous applications. However, most of the schemes compute inner products for bounded length vectors in the sense that a pre-specified bound on the length of predicate/attribute vectors must be fixed while producing the system parameters. On the other hand, an unbounded ZIPE (UZIPE) provides freedom to select the length of vectors at the time of generating keys or producing ciphertexts. The feature of unboundedness expands the applicability of ZIPE in the scenario where the length of vectors varies or is not known in advance. Achieving UZIPE with short secret keys and ciphertexts is the main goal of this paper. More specifically, we present an efficient UZIPE scheme based on symmetric external Diffie-Hellman assumption (SXDH) in the standard model. Our UZIPE enjoys short secret keys and ciphertexts which reduce storage and communication costs. Moreover, we prove security in the adaptively fully attribute-hiding model meaning that the ciphertexts of our UZIPE hide the payload along with the attribute vector. On the technical side, our work takes inspiration from the unbounded inner product functional encryption (UIPFE) of Tomida and Takashima (ASIACRYPT’18) and modifies their framework to UZIPE with efficiency improvements regarding the sizes of ciphertexts and keys. As UIPFE does not generically imply UZIPE, our scheme goes through several technical modifications in the construction and security analysis over the UIPFE.
ROLL: Visual Self-Supervised Reinforcement Learning with Object Reasoning.

Current image-based reinforcement learning (RL) algorithms typically operate on the whole image without performing object-level reasoning. This leads to inefficient goal sampling and ineffective reward functions. In this paper, we improve upon previous visual self-supervised RL by incorporating object-level reasoning and occlusion reasoning. Specifically, we use unknown object segmentation to ignore distractors in the scene for better reward computation and goal generation; we further enable occlusion reasoning by employing a novel auxiliary loss and training scheme. We demonstrate that our proposed algorithm, ROLL (Reinforcement learning with Object Level Learning), learns dramatically faster and achieves better final performance compared with previous methods in several simulated visual control tasks. Project video and code are available at this https URL.
Scale-Consistent Fusion: From Heterogeneous Local Sampling to Global Immersive Rendering

Image-based geometric modeling and novel view synthesis based on sparse large-baseline samplings are challenging but important tasks for emerging multimedia applications such as virtual reality and immersive telepresence. Existing methods fail to produce satisfactory results due to the limitation on inferring reliable depth information over such challenging reference conditions. With the popularization of commercial light field (LF) cameras, capturing LF images (LFIs) is as convenient as taking regular photos, and geometry information can be reliably inferred. This inspires us to use a sparse set of LF captures to render high-quality novel views globally. However, the fusion of LF captures from multiple angles is challenging due to the scale inconsistency caused by various capture settings. To overcome this challenge, we propose a novel scale-consistent volume rescaling algorithm that robustly aligns the disparity probability volumes (DPV) among different captures for scale-consistent global geometry fusion. Based on the fused DPV projected to the target camera frustum, novel learning-based modules (i.e., the attention-guided multi-scale residual fusion module, and the disparity field-guided deep re-regularization module), which comprehensively regularize noisy observations from heterogeneous captures for high-quality rendering of novel LFIs, have been proposed. Both quantitative and qualitative experiments over the Stanford Lytro Multi-view LF dataset show that the proposed method outperforms state-of-the-art methods significantly under different experiment settings for disparity inference and LF synthesis.
Selective Video Encryption Using the Cross Coupling of One-dimensional Logistic Maps

H.264 videos have been the most shared type of video format in recent times and hence its security is a major issue. The techniques presented in the recent times incur complex computations. The major research objective is to design an efficient Chaotic Selective Video Encryption (CSVE) technique which can result in a better visual degradation of the encrypted video with less computational complexity. In the proposed work, in order to secure the H.264 videos, two one-dimensional logistic maps are cross coupled in the chaotic encryption technique which uses a lookup table for data conversion. The technique is evaluated using different performance metrics like Peak Signal to Noise Ratio (PSNR), entropy, statistical analysis etc along with the traditional logistic map. The work is compared with some recent techniques in terms of PSNR and was found out that the proposed technique has better encryption effect.
Multi-Sensorial Human Perceptual Experience Model Identifier for Haptics Virtual Reality Services in Tactful Networking

The tactful networking paradigm is expected to play a crucial role in the next generation networks.Accordingly, adaptive human-aware environments, sensitive to the daily human behavior and individual traits have to be provided, in order to offer a fully immersive and customized experience to users.On the basis of data collected by actual cognitive experiments, this paper proposes a learning framework to discover the multi-sensory human perceptual experience.The paper applies the mixture density network to identify the perception model considering different senses, and then the multi-sensory integration is performed, accordingly to the actual neuro-cognitive model.Furthermore, a supervised learning module has been used to cluster the users on the basis of the human perception identification strategy previously designed, assuming a multimodal structure for the cognitive brain activity.Finally, a practical contextualization has been presented, in relation to the haptics virtual reality services.What emerges from the results is the effectiveness of the tactful approach, i.e., brain-aware, involving the proposed framework, which is validated in comparison to the more conventional brain-agnostic scheme.In fact, the system performance, expressed in terms of reliability in guaranteeing the service exploitation before a target deadline based on the integrated perception, reaches remarkable improvements applying the brain-aware strategy, which exploits the human perception knowledge.