Reviewerly/exHarmony huggingface dataset:
Sound Mixed Reality Prompts for People with Dementia

People with Dementia (PwD) can benefit from mixed reality technologies (MRTs) that can support them in activities of daily living and communication for social engagement, which are vital for their wellbeing and for maintaining good quality of life. Sound MRT prompts are effective in generating correct and successful actions in PwD. However, there is limited research on sound MRT prompts with PwD, who are under-represented demographics in mixed reality research. We studied perceptions of PwD with sound MRT prompts with two Augmented Reality (AR) and two Augmented Virtuality (AV) off-the-shelf technologies. Our results show that PwD were successful at perceiving human voice and sound effects that describe the environment and action carried out by PwD correctly, while the intervention from the researcher created distraction and annoyance in PwD. Music tone required researcher intervention for successful and correct perception. Cartoon voice was correctly perceived, but the results were not statistically significant. PwD also found it annoying and distracting, which affected their engagement with the MRT. We discuss reasons for these differences and provide a framework for designers of prompting MRTs for PwD. This is the first study of MRT sound prompts with PwD in natural or/and home settings.
Code Smell Identification As The Basis For Code Refactoring in The Agricultural Information System Portal Case Study at: Gilangharjo Village, Bantul Regency, Indonesia

Information system technology continues to evolve over time. Various fields of science including agriculture also utilize information system to improve quality and services in the agricultural sector. Indonesia is an agrarian country where most of the populations work in the agricultural sector. Based on this fact, the Indonesian government is also very supportive towards improving and developing technology in agriculture. Dutatani is one of the Agricultural Information Systems (SIP) that has been consistently developed since 2016 towards precision agriculture. One of the technologies developed is web-based technology which has many sub-systems in it. This raises the problem regarding system scalability wherein each sub-system is developed separately and uses a different development model. Each system uses a specific framework and a native. Therefore this study aims to identify which sub-systems are suitable to be developed and refactored to become a new agricultural information system portal. The identification process used a code smell and metric-based approach. The metrics used were Line of Code (LOC), Complexity, Lack of Cohesion of Methods (LCOM) and God Class. From the results of detection using a code smell, the code detected was 55.17%. This study also revealed a fact that code with a good structure would be easier to detect. Modular code that used a framework and was orderly structured could be read well by detection tools, and showed a high LCOM rate compared to structured and nativecode
Improving Quality of Stego Images through Dithering Techniques for Pixel Pair Matching Steganographic Schemes

Steganography plays a vital role in sending any covert data via public media without hiding the covert channel. Due to visually imperceptible embedding process of secret, it is able to escape visual attacks. But when it is suspected, steganography can be detected by any efficient steganalysis techniques. It is an important aspect to improve the security of stego image if the information hidden is highly confidential, by improving the stego image quality. This paper presents a comprehensive analysis of deploying dithering (diffusion) techniques for enhancing the quality of stego images created by various Pixel Pair matching (PPM) steganographic algorithms. The dithering process attempts to keep the difference between original cover image and secret embedded stego image to a very minimum value. The quality of stego images is evaluated on parameters such as Peak Signal to Noise Ratio (PSNR), Structural SIMilarity index (SSIM), Net Pixel Change Rate (NPCR) and Unified Averaged Changed Intensity (UACI). Experimental results show that, depending on the type of steganographic algorithm used, the behaviour of dithering techniques differ and the choice of dithering must be made considering computational complexity of dithering process also.
RusBox: Towards Efficient and Adaptive Sandboxing for Rust

Rust is a new language for safe system programming, and its strong type system and dynamic bound checking guarantee memory safety. Surprisingly, Rust is still vulnerable to buffer overflows, due to its unsafe feature. Recently, there have been a significant amount of studies to protect Rust programs against overflows, however, existing studies have severe limitations: they are either too coarse-grain or of considerable runtime overhead. This paper proposes RUSBOX, a novel sand-boxing software prototype to protect Rust programs against buffer overflow vulnerabilities. The key technical contribution of RUSBOX is its adaptive combination of static program analysis with sandboxing, to make the protection both effective and efficient. To testify the effectiveness of RUSBOX, we apply it to three publicly reported CVEs from real-world Rust projects; to evaluate the cost of RusBox, we plan to apply it to 36 widely used open source Rust projects.
A Mixed-Integer Program for Drawing Orthogonal Hyperedges in a Hierarchical Hypergraph

This paper presents a new formulation and solution of a mixed-integer program for the hierarchical orthogonal hypergraph drawing problem, and the number of hyperedge crossings is minimized. The novel feature of the model is in combining several stages of the Sugiyama framework for graph drawing: vertex ordering, the assignment of vertices’ x-coordinates, and orthogonal hyperedge routing. The hyperedges of a hypergraph are assumed to be multi-source and multi-target, and vertices are depicted as rectangles with ports on their top and bottom sides. Such hypergraphs are used in data-flow diagrams and in a scheme of cooperation. The numerical results demonstrate the correctness and effectiveness of the proposed approach compared to mathematical heuristics. For instance, the proposed exact approach yields a 67.3% reduction of the number of crossings compared to that obtained by using a mathematical heuristic for a dataset of non-planar graphs.
Seismic Event Identification Based on a Generative Adversarial Network and Support Vector Machine

Identifying appropriate seismic events is the primary precondition for conducting meaningful analysis in seismological research. The successful creation of a method to automatically identify earthquakes from large amounts of data has become increasingly vital, especially with the construction of seismic stations, the collection of extensive seismic data, and the development of earthquake early warning (EEW) systems. To accurately identify seismic events, a combined model based on a generative adversarial network (GAN) and a support vector machine (SVM) is proposed to distinguish between earthquakes and microtremors. We first use 52,537 strong ground motion records from Japan to train a GAN and extract the characteristics of P waves and then use an SVM to discriminate seismic events in the testing set, thereby transforming the complex seismic event identification into a simpler binary classification of earthquakes and microtremors. The results illustrate that the combined model can achieve accuracies of 99.74% for P waves and 99.93% for microtremors, which represents an increase in accuracy of 14.13% compared with the traditional short-term averaging/long-term averaging (STA/LTA) method. Additionally, 98% of the local seismic events in the Great East Japan earthquake were identified. Therefore, the combined model has a wide range of applications in EEW and earthquake monitoring.
Survey of Blockchain Techniques for IoT Device Security

The Internet of Things (IoT) is gaining popularity across sectors. With the initiation of emerging technologies like 5G, interconnected devices are on the rise. Healthcare is another critical sector using IoT for healthcare devices and lifesaving equipment. With the augmented diffusion of the IoT in various spheres of life, concerns have been raised around security. Due to IoT’s inherent technical limitations, it continues to be a low hanging fruit for hackers. Many solutions have been proposed and implemented to address the security concerns, and one such solution is the use of blockchain to address the security limitations and concerns of IoT. Blockchain today is being adopted in various industries, including the financial sector, because of its security features. Blockchain supports decentralized and distributed architecture along with enhanced security. Blockchain hence offers possibilities of addressing the security concerns of IoT. This chapter explores the security challenges being faced by IoT and the utilization of blockchain in addressing some of the security concerns of IoT. The challenges related to IoT security and possible solutions using blockchain have been discussed in this chapter.
Evaluating Engagement in Technology-Supported Social Interaction by People Living with Dementia in Residential Care

Technologies can support the well-being of people living with dementia in residential care by fostering meaningful interactions through multimodal and playful features. This article presents the evaluation of A Better Visit, an app that aims at engaging residents and visitors in shared activities. We define engagement in terms of dyadic interactions with the interface of the device, and interactions within the social context of the care home. We generated two Motivational Models that describe different perspectives of how people engaged with the system: the first was built with the design team, and the second was built using the first Motivational Model as a sensitising concept for the analysis of data from four care homes that used the app for periods of three months. Our findings show that the facilitation of activities plays a relevant role for engaging in social interaction. We discuss how technologies can contribute to engaging people living with dementia in social interaction: firstly, facilitation can be understood as a concept that is supported by people and technology. Secondly, we discuss the influence of the social context of the setting and the understandings of the individual in how people assess the engagement of the residents in social interaction.
No-Reference Quality Assessment of Pan-Sharpening Images with Multi-Level Deep Image Representations

The Pan-Sharpening (PS) techniques provide a better visualization of a multi-band image using the high-resolution single-band image. To support their development and evaluation, in this paper, a novel, accurate, and automatic No-Reference (NR) PS Image Quality Assessment (IQA) method is proposed. In the method, responses of two complementary network architectures in a form of extracted multi-level representations of PS images are employed as quality-aware information. Specifically, high-dimensional data are separately extracted from the layers of the networks and further processed with the Kernel Principal Component Analysis (KPCA) to obtain features used to create a PS quality model. Extensive experimental comparison of the method on the large database of PS images against the state-of-the-art techniques, including popular NR methods adapted in this study to the PS IQA, indicates its superiority in terms of typical criteria.
Blockchain Based Registration Model for Higher Education

Education is one of the activities carried out by students and students in order to obtain a title. The process that is common in edification method in Indonesia is a legal action that is done via academic credit at school institutions and must be completed if you want to get recognition in the issuance of a diploma. In Indonesia, a learning activity is full of semi-computer processes but not all educational institutions have implemented this technology. In cases like this, an individual may look for loopholes to commit fraud, such as falsifying diplomas. To find a solution to the problems above, we need to digitize, what is it? Digitization is one of the system that can offer a sense of security and can minimize bureaucracy in the document legal action of ratification with savings on retention and manpower used. A tips passed by students before receiving an academic diploma at one of the universities are starting with the registration process, after all the registration processes are completed, a student is officially accepted at the university and can directly participate in teaching and learning activities , at the time of graduation, it is certain that a student must pass all courses and have completed all the tasks that have been given until he is officially declared graduated. Problems that often occur in the field of education, especially when it is in the registration process, are data falsification, data manipulation, and even the worst case is data loss, this fraud is usually carried out by irresponsible parties. This journal aims to submit a proposal for the schoolboy registry process of school institute in digitalization education method in Indonesia using Blockchain technology. This study resulted in a student enrollment model in an educational institution using Blockchain to supply transparent and validated data in educational process.
The effectiveness of discrete hermite wavelet filters technique in digital image watermarking

&lt;span&gt;In this work, new discrete wavelets were derived Hermite polynomials for obtained discrete hermite wavelet transformation (DHWT), and their efficiency for use in image processing is demonstrated by proving the realization of important theorems. Moreover, the role of the new and proposed waveforms in their effective effect in placing the watermark with the color image is clarified, and a program was created using MATLAB software by creating a subprogram for constructing the new wavelet and proving its efficiency with an analytic image. The process is repeated using DHWT to analyze the image. The color image has been subjected to various attacks after which the watermark is retrieved from the image after comparing it with the proposed algorithm and it has proven its power faster and better than the previously suggested methods. The final conclusion shows that using new wavelets DHWT better peak signal of noise ratio (PSNR)s can be obtained and that the proposed algorithm fills in better the lack of awareness of the watermark and its strength under different attacks.&lt;/span&gt;
An Adaptive Self-Organizing Migration Algorithm for Parameter Optimization of Wavelet Transformation

Wavelet transformation is well applied in the field of image processing, and parameter optimization of wavelet transformation has always been an eternal topic on its performance improvement. In this paper, an adaptive self-organizing migration algorithm (ASOMA) is proposed to optimize the wavelet parameters to elevate the performance of wavelet denoising. Firstly, based on the original SOMA, an adaptive step size adjustment method is proposed by recording the step information of successful individuals, which improves the search ability of the SOMA. Secondly, an exploratory selection method of leader is proposed to effectively balance the exploration and exploitation of the SOMA. Finally, ASOMA is compared with the original SOMA and its variants using wavelet general threshold denoising on classical test images in denoising performance, which is evaluated by the indicators of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The experimental results demonstrate that ASOMA has better denoising performance than the wavelet general threshold, the original SOMA, and the related variants of SOMA.
Multi-feature based automatic facial expression recognition using deep convolutional neural network

&lt;p&gt;&lt;span&gt;Deep multi-task learning is one of the most challenging research topics widely explored in the field of recognition of facial expression. Most deep learning models rely on the class labels details by eliminating the local information of the sample data which deteriorates the performance of the recognition system. This paper proposes multi-feature-based deep convolutional neural networks (D-CNN) that identify the facial expression of the human face. To enhance the accuracy of recognition systems, the multi-feature learning model is employed in this study. The input images are preprocessed and enhanced via three filtering methods i.e., Gaussian, Wiener, and adaptive mean filtering. The preprocessed image is then segmented using a face detection algorithm. The detected face is further applied with local binary pattern (LBP) that extracts the facial points of each facial expression. These are then fed into the D-CNN that effectively recognizes the facial expression using the features of facial points. The proposed D-CNN is implemented, and the results are compared to the existing support vector machine (SVM). The analysis of deep features helps to extract the local information from the data without incurring a higher computational effort.&lt;/span&gt;&lt;/p&gt;
Improved parametrized multiple window spectrogram with application in ship navigation systems

In analyzing non-stationary noisy signals with time-varying frequency content, it's convenient to use distribution methods in joint, time and frequency, domains. Besides different adaptive data-driven time-frequency (TF) representations, the approach with multiple orthogonal and optimally concentrated Hermite window functions is an effective solution to achieve a good trade-off between low variance and minimized stable bias estimates. In this paper, we propose a novel spectrogram method with multiple optimally parameterized Hermite window functions, with parameterization which includes a pair of free parameters to regulate the shape of the window functions. The computation is performed in the optimization process to minimize the variable projection (VP) functional problem. The proposed parametrized distribution method improves TF concentration and instantaneous frequency (IF) estimation accuracy, as shown in experimental results for synthetic signals and real-life ship motion response signals. With the optimization of nonlinear least-squares approximation of the ship response signals, the Hermite spectra are centralized, and only up to 15 basis functions are sufficient for concentration improvement in the TF domain.
Emotional analysis of safeness and risk perception of transports and travels by car and motorcycle in London and Rome during the COVID-19 pandemic

The estimation of perceived safeness and risk by people is especially invaluable for security and safety managing. Everyone is based on the opinion of other persons to do selections of any kind and the Internet personifies the location where these opinions are primarily tracked, obtained, and assessed. From this point of view, social networks are considered of huge impact. For this reason, Opinion Mining and Sentiment Analysis reached remarkable applications in a diversity of circumstances and one of the most remarkable is expressed by public security and safety. The purpose of the present work is to investigate the perception of risk with transports and travels by car and motorcycles in London (UK) and Rome (Italy) during COVID-19 pandemic, from March 23 to July 9, 2020, underlighting the emotional components in three distinct pandemic phases of the considered period in the two countries, via the semantic analysis of the textual contents existing in Twitter.
Design of type‐2 fuzzy logic controller optimized with firefly algorithm for maximum power point tracking of photovoltaic system based on super lift Luo converter

Abstract The efficiency is a very important parameter for photovoltaic systems. Positive output elementary super lift Luo converter classified as high gain DC‐DC converter are used in photovoltaic systems because of their advantages such as high efficiency, reduced ripples, and low switching loss. The drawback of the photovoltaic system based on this converter is its sensitivity to small changes on the control signal. Both maximum power point tracking and control signal stability require a powerful control system as this is a challenging task for the photovoltaic system. Interval type‐2 fuzzy logic systems have recently been utilized in many control processes due to their ability to model uncertainties better than type‐1 fuzzy logic systems. In this work, interval type‐2 Takagi–Sugeno–Kang fuzzy logic controller was first designed as a controller. Then, the controller, which was proposed for this paper, was optimized with the Firefly Algorithm to improve the control performance. Performance of controllers is evaluated via comparative simulation studies under the most demanding environmental conditions. It has been observed that the proposed controller reduces the settling time by more than 50% and increases the efficiency up to 10% according to the simulation situations.
Content-Oblivious Trust and Safety Techniques

We present the results of a survey about the trust and safety techniques of a group of online service providers that collectively serve billions of users. We classify techniques that require the provider to be able to access the contents of users’ files and communications at will as content dependent, and content oblivious otherwise. We find that more providers use abuse-reporting features (which are content oblivious) than other abuse-detection techniques, but that participants’ abuse-reporting tools do not consistently cover the types of abuse that users may encounter. We also find that, despite strong consensus among participating providers that automated content scanning (which is content dependent) is the most useful means of detecting child sex abuse imagery, they do not consider it to be nearly as useful for other kinds of abuse. These results indicate that content-dependent techniques do not constitute a silver bullet to protect users against abuse. They also demonstrate that the impact of end-to-end encryption (which, controversially, impedes outside access to user content) on abuse detection may vary by abuse type. These findings have implications for policy debates over the regulation of online service providers’ anti-abuse obligations and their use of end-to-end encryption.
The Performance of Thai Sign Language Recognition with 2D Convolutional Neural Network Based on NVIDIA Jetson Nano Developer Kit

Thai Sign Language Recognition is a Thai Sign Language learning computer recognition. The system constructs an architecture of T-SLR by TSR- 2DCNN based on NVIDIA Jetson Nano Developer Kit. It is a novelty of automatic translation TSL innovation and reveals the performance of feature extraction and classification to reduce crashed system, overloaded or automatic reboot while complicated processing occurs. The dataset contains 7 gestures in TSL, training images are 7,000 images and validation images are 700 images. The result compares with many techniques as shown that TSR-2DCNN can increase the performance of TSLR in real-time, effectiveness with an accuracy of 0.9914 and loss of 0.03537.