Reviewerly/exHarmony huggingface dataset:
A discriminant kernel entropy-based framework for feature representation learning

The intelligent multimedia processing community has developed increasing interest in kernel entropy component analysis (KECA) due to its abilities in effective data transformation and dimensionality reduction. However, since only the unsupervised structural information of Renyi entropy from the given data set is utilized, KECA alone is incapable of generating high quality discriminant features. Aiming to develop a new and generic approach for feature representation learning, this paper proposes a discriminant kernel entropy-based framework, which integrates KECA and a complete discriminant strategy (consisting of regular and irregular discriminant information), to explore discriminant feature representations from the given data set. The framework is realized and further optimized to generate a more powerful discriminant descriptor for feature representation learning, leading to improved performance. Since the joint utilization of kernel entropy estimation and the complete discriminant strategy is able to reveal the distribution and semantic information of the given data, the proposed framework opens up a new front for discriminant feature representation learning via information theoretic learning (ITL). To demonstrate the generic nature and effectiveness of the proposed framework, experiments are conducted on two different data sources; the visual data source (e.g., University of California Irvine (UCI) database, Olivetti Research Lab (ORL) database, Caltech 256 database) and the audio data source (Ryerson Multimedia Lab (RML) audio emotion database). The results show this framework yields superior performance over other methods on the data sets evaluated for feature representation learning.
SceneAR: Scene-based Micro Narratives for Sharing and Remixing in Augmented Reality

Short-form digital storytelling has become a popular medium for millions of people to express themselves. Traditionally, this medium uses primarily 2D media such as text (e.g., memes), images (e.g., Instagram), GIFs (e.g., Giphy), and videos (e.g., TikTok, Snapchat). To expand the modalities from 2D to 3D media, we present SceneAR, a smartphone application for creating sequential scene-based micro narratives in augmented reality (AR). What sets SceneAR apart from prior work is its ability to share the scene-based stories as AR content. No longer limited to sharing images or videos, users can now experience narratives in their own physical environments. Additionally, SceneAR affords users the ability to remix AR content, empowering them to collectively build on others’ creations. We asked 18 people to use SceneAR in a three-day study, and based on user interviews, analyses of screen recordings, and the stories they created, we extracted three themes. From these themes and the study overall, we derived six strategies for designers interested in supporting short-form AR narratives.
A Systematic Mapping Study on the Use of Software Engineering Practices to Develop MVPs

[Background] Many startup environments and even traditional software companies have embraced the use of MVPs (Minimum Viable Products) to allow quickly experimenting solution options. The MVP concept has influenced the way in which development teams apply Software Engineering (SE) practices. However, the overall understanding of this influence of MVPs on SE practices is still poor. [Objective] Our goal is to characterize the publication landscape on practices that have been used in the context of software MVPs. [Method] We conducted a systematic mapping study using a hybrid search strategy that consists of a database search and parallel forward and backward snowballing. [Results] We identified 33 papers, published between 2013 and 2020. We observed some trends related to MVP ideation and evaluation practices. For instance, regarding ideation, we found six different approaches (e.g., Design Thinking, Lean Inception) and mainly informal end-user involvement practices (e.g., workshops, interviews). For evaluation there is an emphasis on end-user validations based on practices such as usability tests, A/B testing, and usage data analysis. However, there is still limited research related to MVP technical feasibility assessment and effort estimation. We also observed a lack of scientific rigor in many of the identified studies. [Conclusion] Our analysis suggests that there are opportunities for solution proposals to address gaps concerning technical feasibility assessment and effort estimation. Also, more effort needs to be invested into empirically evaluating the existing MVP-related practices.
An improved sine–cosine algorithm with dynamic selection pressure

Sine cosine algorithm (SCA) is a newly developed optimization tool that has received increasing attention in the field of computational intelligence. Nevertheless, SCA has a combination of strong exploration and weak exploitation abilities, which undermines the performance of the algorithm. A dynamic selection strategy (DSS) has been proposed in this paper to overcome this limitation. The central idea is that the selection pressure of the parent is dynamically regulated during the course of evolution, and the parent with higher fitness have higher selection probability. Accordingly, the weak exploitation ability of SCA can be enhanced. This approach can be readily applied to other SCA variants as well. Results from systematic experiments conducted on CEC2014 competition benchmark suite show that the proposed DSS method can significantly improve the performance of the SCAs. In addition, the proposed DSS-SCA algorithm has been successfully applied to six engineering design problems, which shows that the algorithm has the potential to solve other practical problems.
Strategies and Tools for Effective Suspicious Event Detection from Video: A Survey Perspective (COVID-19)

Safety and health security are the major concerns in today's modern world. Most of the countries have adopted camera surveillance systems to achieve a secure environment, one such example is restriction imposed for the movement of people during the COVID-19 pandemic. Thus, surveillance systems serve the purpose of humans to identify intruders with suspicious behavior. Detecting these intrusions or any suspicious events in an early stage from surveillance systems is an important and challenging task. This can be done using Suspicious Event Detection Models (SEDM) and tools. In earlier systems, it is found that machine learning methods are proved to be efficient in predicting the suspicion activities. In this chapter, a survey of various SEDM Strategies and Tools that were developed earlier which captures the suspicious events in campuses or societies is discussed that provides a Full-proof secure environment. Few, earlier SEDM have also used deep learning approaches, IoT, and fuzzy logic techniques. Finally, an improved SEDM for campuses based on deep learning is suggested. The capability of deep learning (CNN) method is very influential in extraction of features from unstructured contents, especially from captured images of video. The efficiency of this suggested SEDM will be better when compared with earlier state-of-art-systems, which do not support alarming system by making use of GPS, and then extracting personal details.
Modulation recognition of composite modulation signal based on two-fold digital receiver and goodness of fit test

Aiming at the modulation recognition problem of mixed signal set {BPSK, QPSK, BPSK-FM, QPSK-FM} with unknown parameters, this paper proposes a modulation recognition algorithm based on two-fold PLL (phase locked loop) and goodness of fit test. The PLL is used to extract the features of the composite modulation signal, and the goodness of fit test is used to measure the distance of the phase cumulative distribution of the demodulation information. Theoretical analysis and simulation results show that the performance of the recognition method based on two-fold PLL and goodness of fit test is better than the widely used modulation recognition method based on constellation.
Probabilistic Multimodal Optimization

Multimodal optimization, which aims to discover multiple satisfactory solutions simultaneously, has attracted increasing attention from researchers in the evolutionary computation community. With the aid of niching methods, evolutionary algorithms could simultaneously locate multiple satisfactory solutions in a single run. Although many multimodal evolutionary algorithms have been developed, they are confronted with two limitations: (1) in the niching stage, most niching-based multimodal methods need to compute pairwise Euclidean distances between individuals to separate the population into species, which gives rise to a high computational burden; and (2) in the optimization stage, most existing multimodal algorithms may have limitations in exploring the solution space, due to the utilization of traditional individual-based meta-heuristics, which may easily get trapped in local areas. To resolve the above issues, in this chapter, we introduce probabilistic multimodal optimization algorithms by presenting two probability-based frameworks for multimodal optimization. More specifically, we present a probability-based niching framework to accelerate the niching speed and a probability-based optimization framework to promote the optimization efficiency of multimodal algorithms, respectively. In the former, we utilize locality sensitive hashing to project individuals into buckets with probabilities to divide the population into species. Such a framework can be embedded into different niching methods to accelerate the niching speed. In the latter, we take advantage of the probability distribution of individuals to evolve the population along with a novel adaptive local search method. To instantiate these two frameworks, we customize them using two distinct approaches, respectively. More concretely, we embed the former into locally informed particle swarm optimization (LIPS) and neighborhood-based crowding differential evolution (NCDE), and customize the latter utilizing an explicit probability-based algorithm, the estimation of distribution algorithm (EDA), and an implicit probability-based algorithm, the continuous ant colony optimization algorithm (ACO), respectively. The efficiency and effectiveness of these two frameworks are carefully examined on a widely used multimodal benchmark set by means of comparing the associated customized algorithms with state-of-the-art multimodal methods. Lastly, the application of the proposed algorithms on multiple pedestrian detection problems is also presented. Experimentally, our approach is seen to perform competitively with traditional methods in this domain.
Towards Real-Time Drone Detection Using Deep Neural Networks

The growing use of drones/unmanned aerial vehicles (UAV’s) has generated in the general public as well as in institutions the awareness of the possible misuse that can be given to these devices. For this reason, the detection of drones has become a necessity that poses in the case of detection using images a great difficulty due to its small size, in addition to the background that can make detection even more difficult. Regarding this work, the aim is not only to detect drones but also to distinguish the type of detected drone. Different networks were trained whereupon an analysis was conducted in which it was found that the YOLOv4tiny network with 416 × 416 input resolution presents the best results considering both the inference performance and the processing speed.
A Taxonomy of Multimedia-based Graphical User Authentication for Green Internet of Things

Authentication receives enormous consideration from the research community and is proven to be an interesting field in today’s era. User authentication is the major concern because people have their private data on devices. To strengthen user authentication, passwords have been introduced. In the past, the text-based password was the traditional way of authentication, but this method has particular shortcomings. The graphical password has been introduced as an alternative, which uses a picture or a set of pictures to generate a password. In the future, it is a requirement of such approaches to maintain robustness and consume fewer energy resources to become suitable for the Green Internet of Things (IoT). Similarly, diverse graphical password authentication mechanisms have been used to provide users with better security and usability. In this article, we conduct an extensive survey on the existing approaches of graphical password authentication to highlight the challenges required to be addressed for Green IoT. In comparison to other existing surveys, the objective is to consolidate the graphical password technique and to identify the problem associated with it. Besides, this survey will also identify the vulnerabilities of the graphical password against several potential attacks. We have also examined the strengths and weaknesses of each technique along with the future research directions. This study also evaluates the usability of each approach by considering learnability, memorability, and so forth and also presents a comparative analysis with security.
Integrated Use of Data Mining Techniques for Personality Structure Analysis

Data mining techniques include traditional statistical estimation theory and machine learning methods. They have been intensively developed in recent years, and are increasingly used in a variety of fields. The present research suggests the simultaneous, complex use of different data mining methods. The approach is applied to psycho diagnostic data processing. The traditional methods of factor analysis and cluster analysis are used together, along with artificial neural networks as one of the foundations of artificial intelligence. The possibilities of this approach are demonstrated by the example of adolescents' psycho diagnostic data processing. The study is based on structural Cattelian Personality Theory as the most well-known example using the factor model of personality. Based on the complex processing of psycho diagnostic data, it is shown that each of the data mining methods separately does not provide complete information about the relationship between the psychological characteristics of the respondents. Comparing the results obtained by different processing methods allows us to obtain information about the intra-system relationships of psychological qualities, to assess the hierarchy and significance of personal psychological characteristics. It is shown that the analysis can be carried out with widely used software and on a small sample of respondents of several dozen people. This allows psychologists to analyze the psychological characteristics of homogeneous social groups of respondents whose psycho diagnostic results were obtained under experimental conditions at the same time.
VTLayout: Fusion of Visual and Text Features for Document Layout Analysis

Documents often contain complex physical structures, which make the Document Layout Analysis (DLA) task challenging. As a pre-processing step for content extraction, DLA has the potential to capture rich information in historical or scientific documents on a large scale. Although many deep-learning-based methods from computer vision have already achieved excellent performance in detecting Figure from documents, they are still unsatisfactory in recognizing the List, Table, Text and Title category blocks in DLA. This paper proposes a VTLayout model fusing the documents’ deep visual, shallow visual, and text features to localize and identify different category blocks. The model mainly includes two stages, and the three feature extractors are built in the second stage. In the first stage, the Cascade Mask R-CNN model is applied directly to localize all category blocks of the documents. In the second stage, the deep visual, shallow visual, and text features are extracted for fusion to identify the category blocks of documents. As a result, we strengthen the classification power of different category blocks based on the existing localization technique. The experimental results show that the identification capability of the VTLayout is superior to the most advanced method of DLA based on the PubLayNet dataset, and the F1 score is as high as 0.9599.
Nom Document Background Removal Using Generative Adversarial Network

In this research, we present a new technique to improve the performance of a Nom-character recognition system. Nom-character recognition is a challenging problem in pattern recognition. Especially these characters are not only blurred or distorted in a paper of a historical document containing ink strokes and symbols created by readers. Generative Adversarial Network (GAN) is one of the advanced versions of deep neural networks applied to generate artificial photos of objects [28]. Many versions of GAN have been malfunctioned recently to help the learning process be more stable and realistic to maximize features extracted from the data. We have been using a recent version of GAN to extract characters from images with complex backgrounds and brightness. This task is to retrieve clean text images from complex and noisy background sources. To the best of our knowledge, we perform the test on the Nom Dataset, which characterizes by multiple noise forms. The results demonstrate that this approach can help to improve any Nom-character recognition system.
Shielded Computations in Smart Contracts Overcoming Forks

In this work, we consider executions of smart contracts for implementing secure multi-party computation (MPC) protocols on forking blockchains (e.g., Ethereum), and we study security and delay issues due to forks. In this setting, the classical double-spending problem tells us that messages of the MPC protocol should be confirmed on-chain before playing the next ones, thus slowing down the entire execution. Our contributions are twofold: By reducing the number of confirmations, our protocols can be significantly faster than natural constructions.
Sentiment Analysis of Pancasila Values in Social Media Life Using the Naive Bayes Algorithm

The implementation of Pancasila in citizenship from year to year has not found good news, even in mid-2020 there were many conflicts due to polemics carried out by the Pancasila Ideology Development Agency in the bill. Various efforts have been made by both state officials and activists to make people aware of the importance of implementing Pancasila values in social life. Therefore, this study aims to determine whether a person has Pancasila spirit or not in terms of that person&#x0027;s social media. The research will result in the accuracy of Naive Bayes classification using the media in the form of tweets reaching 73&#x0025; with the results in the form of 3 classes, namely Neutral, Positive and Negative Pancasila. The results of this study have resulted and can be applied to classify someone with Pancasila spirit or not by using media in the form of social media accounts with the Naive Bayes method, being able to detect early as a basis for decision making by stakeholders and can trigger someone to evaluate himself, apply Pancasila values in every activity, even on social media.
Improving a Conversational Speech Recognition System Using Phonetic and Neural Transcript Correction

This article describes the successful implementation of a conversational speech recognition system applied to telephonic sales performed by an autonomous agent. Our implementation uses a post-processing corrector based on phonetic representations of text and subsequent neural network classifier. The classifier assesses the proposed correction’s relevance to reduce the errors in the transcript sent to a downstream Natural Language Understanding engine. The experiments were carried on correcting transcripts from real audios of orders placed by customers of a large bottling company. We measured the Word Error Rate of the corrected transcripts against human-annotated ground-truth to verify the improvement produced by the system. To evaluate the corrections’ impact on the entities detected by the Natural Language Understanding engine, we used Jaccard distance, Precision, Recall, and \(F_1\). Results show that the implemented system and architecture enhance the transcript relative Word Error Rate on a 39% and Jaccard distance on 13% in comparison to the Automatic Speech Recognition baseline, making them suitable for real-time telephonic sales systems implementation.
Experimental Study of Fault Injection Attack on Image Sensor Interface for Triggering Backdoored DNN Models

A backdoor attack is a type of attack method inducing deep neural network (DNN) misclassification. An adversary mixes poison data, which consist of images tampered with adversarial marks at specific locations and of adversarial target classes, into a training dataset. The backdoor model classifies only images with adversarial marks into an adversarial target class and other images into the correct classes. However, the attack performance degrades sharply when the location of the adversarial marks is slightly shifted. An adversarial mark that induces the misclassification of a DNN is usually applied when a picture is taken, so the backdoor attack will have difficulty succeeding in the physical world because the adversarial mark position fluctuates. This paper proposes a new approach in which an adversarial mark is applied using fault injection on the mobile industry processor interface (MIPI) between an image sensor and the image recognition processor. Two independent attack drivers are electrically connected to the MIPI data lane in our attack system. While almost all image signals are transferred from the sensor to the processor without tampering by canceling the attack signal between the two drivers, the adversarial mark is injected into a given location of the image signal by activating the attack signal generated by the two attack drivers. In an experiment, the DNN was implemented on a Raspberry pi 4 to classify MNIST handwritten images transferred from the image sensor over the MIPI. The adversarial mark successfully appeared in a specific small part of the MNIST images using our attack system. The success rate of the backdoor attack using this adversarial mark was 91%, which is much higher than the 18% rate achieved using conventional input image tampering.
The connection of a Blockchain with Students’ Attendance Register based on Security Cameras

These days, security cameras perform surveillance tasks in institutions of education. Thanks to the utilization of analytic functions, such as headcount, face detection, face recognition, and the Black and White List, a possibility is given for composing an attendance register of the students visiting the lectures.To attain secure data storage, the utilization of decentralized blockchain technology is expedient. The most optimal solution is that the university would constitute a blockchain, so the institution would regulate the rules of the operation itself.To link the blockchain with the NVR (Network Video Recorder) unit, and to have the attendance register composing system running, a smart contract is to be implemented in practice. By the closing of a smart contract, great care should be applied to the input of valid data.