Reviewerly/exHarmony huggingface dataset:
Does individual gaze lead to individual visual representations?

Individuals show strong and reliable differences in their eye movements towards static (e.g. Bargary et al., 2017, de Haas et al., 2019) and dynamic (Constantino et al., 2017) scenes. Here, we find that this also applies to observers in an eyetracking experiment (n = 16) watching a feature film (‘Shaun the Sheep’), who varied up to factor 2 regarding saccadic rates and amplitudes. What are the consequences of such differences? Does individual gaze lead to individually different ventral representations of the same external events? Or does individual gaze aid inter-individually more similar representations by compensating for differences at earlier stages of processing (such as individual visual field geometry; Moutsiana et al., 2016)? In order to juxtapose these hypotheses, we used functional MRI in a subset of participants (current n = 4; data collection ongoing). Participants watched segments of the movie as in the eyetracking experiment in the scanner, either freely moving their eyes or fixating centrally. We then used a customized version of hyperalignment (Haxby et al., 2011), in order to test how predictive inferior temporal representations of one observer were for those of another. This was done separately for each pair of observers and condition. Pilot MRI results (n = 4 participants) show a trend for better cross-person decoding in the central fixation condition (t = 2.49, p = .054). This would be in line with individual gaze leading to distinct representations of the visual world. We plan to test this hypothesis in a full sample and build a computational model to probe the role of several facets of gaze for representational similarity.
Visual orienting to beyond field of view targets in 3D space: Effects of cue modality, eccentricity, and distractor presence

Visual search in operational environments necessitates simultaneous head and eye movements (gaze shifts) to scan large portions of the visual field where objects and people vary in spatial position. Research has shown that auditory cues are more efficient than visual cues when orienting people to targets within their immediate field of view. Much of this work, however, has been performed in a limited two-dimensional space with little application to naturalistic search behaviors in a three-dimensional, immersive environment. This experiment focused on cueing targets beyond a person’s current field of view within a 360-degree virtual reality environment and comparing performance between spatialized auditory and visual cues. Participants responded to the orientation of a Gabor target (2.6 cycles/deg) which could appear at spatial locations varying in eccentricity. Targets were presented both with and without the presence of heterogenous distractors. The visual cue was a responsive three-dimensional mini-map at the bottom of the participant’s screen that indicated the location and elevation of the target’s location relative to the viewer. The spatialized auditory cue (accomplished using a standard head-related transfer function) was a constant frequency (1040 Hz) that increased in volume as the user’s gaze became closer to the target location. Both the visual and auditory cues provided continuous feedback to the searcher based on current gaze position. Here, we present data (n=20) to evaluate cue efficiency as a function of the cue modality, target eccentricity, and distractor presence by comparing the orienting response and precision of target localization. To this end, we compare the differences in gaze shifts (head movement initiation time and saccadic reaction time) and search performance (accuracy and localization time). The results suggest that the auditory and visual cues have different impacts on search behavior.
15 Questions - Debashis Sinha talks about sound

&lt;p&gt;A feature interview about Debashis Sinha's sound practice on the website 15 Questions.&lt;/p&gt; &lt;p&gt;&lt;br&gt;&lt;/p&gt;
Extended Cloud Topologies

In this chapter, we will cover extended cloud topologies. The topology of a cloud is the general interconnectedness of physical and logical components of a cloud.
Computer Vision in Streamlit

In this final chapter of the book, we will see one small example of a computer vision application that can predict what animals are in an image. This application will deploy our trained model to classify animal images.
Bitcoin Transaction Fees, Miners' Revenue, Concentration and Electricity Consumption: A Failing Ecosystem

The research and investment community seems to ignore the long-term sustainability of Bitcoin, which is reflected in four flaws: transaction fees, miners' revenue, concentration and electricity consumption. While most of the authors have aimed to examine one topic at a time, with a particular interest in electricity consumption and carbon footprint, the aim of this paper is to examine all these issues simultaneously to provide a more comprehensive view on long-term sustainability of Bitcoin. This paper looks at these flaws and reveals why Bitcoin is not sustainable in the long run, how decentralization is being lost, how the design is putting artificial and unrealistic pressure on the ecosystem, while all being powered by an unjustifiable amount of dirty electricity sources. Our main findings are as follows. Firstly, transaction fees are already high and set to increase in time, further discriminating small transactions against big ones. Secondly, miners' revenue comes mostly from the block reward. The block reward is the main income source for miners, but is set to be cut on a regular basis, making miners' revenue not sustainable in the long run. Thirdly, miner concentration is already an issue, with a possibility of deepening even more and diminishing the idea of decentralization. Fourthly, the high electricity demand and the associated carbon footprint thus cannot be justified by any means. We deem our results useful for overall policy and regulatory implications.
Formalizing and Safeguarding Blockchain-Based BlockVoke Protocol as an ACME Extension for Fast Certificate Revocation

Certificates are integral to the security of today’s Internet. Protocols like BlockVoke allow secure, timely and efficient revocation of certificates that need to be invalidated. ACME, a scheme used by the non-profit Let’s Encrypt Certificate Authority to handle most parts of the certificate lifecycle, allows automatic and seamless certificate issuance. In this work, we bring together both protocols by describing and formalizing an extension of the ACME protocol to support BlockVoke, combining the benefits of ACME’s certificate lifecycle management and BlockVoke’s timely and secure revocations. We then formally verify this extension through formal methods such as Colored Petri Nets (CPNs) and conduct a risk and threat analysis of the ACME/BlockVoke extension using the ISSRM domain model. Identified risks and threats are mitigated to secure our novel extension. Furthermore, a proof-of-concept implementation of the ACME/BlockVoke extension is provided, bridging the gap towards deployment in the real world.
USING READING SUB-SKILLS TO IMPROVE THE NON-MAJOR FRESHMEN’S ABILITY OF INDEPENDENT READING CỎMPREHENSION AT TAN TRAO UNIVERSITY

Based on the current educational perspective, the main goal of learning English is to form and develop the essential knowledge and skills of English and the necessary intellectual qualities to help students improve or apply to life (Dang Thi Lan (2009), [1]. Therefore, all new English books are compiled according to the current educational program, based on the criteria of promoting active learning methods, focusing on developing all four skills: listening, speaking, reading and writing in each lesson. However, through practical teaching, it has been shown that the English reading comprehension ability of the first-year students is still limited, not actively grasping the primary information of the text, not understanding all the implications of the reading, leading to the birth of students. Students learn reading comprehension skills that are not effective. Besides, in reading comprehension iiilessons, iiiteachers iiistill iiiface iiimany iiidifficulties iiibecause iiithey iiihave iiinot iiifully iiimastered iiithe iiitricks iiiof iiiteaching iiireading iiicomprehension. iiiIn iiithis iiistudy, iiithe iiiauthor iiihopes iiito iiipartly iiihelp iiistudents iiiovercome iiicommon iiichallenges iiito iiipractice iiiEnglish iiireading iiicomprehension iiiskills iiimore iiieffectively, iiisuch iiias iiireading iiicomprehension iiitexts iiiand iiireading iiicomprehension iiiexercises iiiwith iiisuccessful iiiresults. iiiHigh... iiiFrom iiithere, iiitake iiithe iiiinitiative iiiin iiiabsorbing iiiand iiiperceiving iiithe iiilesson's iiiknowledge.
REVIEW IN IMAGES ENHANCEMENT MEDICAL USING CURVELET TRANSFORM AND VECTOR INFORMATION

The use of medical imaging for the purposes of medical monitoring and diagnosis has seen tremendous growth in recent years, which has resulted in a rising need for suitable data storage and accessibility. The primary goal of image enhancement is to generate a processed image that is suitable for a specific application. This can be accomplished by acquiring the image in the highest resolution possible or by using a variety of techniques to extract the important information while ignoring the irrelevant information. Image processing can then be performed on the processed image. Image enhancement techniques are used in medical imaging to increase the quality of the visual representation of an image by reducing the amount of noise present and sharpening the features. This study examines the Curvelet transform-based approach for increasing medical image quality. The contribution of the proposed review two steps are advised for optimizing the images. The first step is the optimization step utilizing the Curvelet transform, which featured a series of image-preparation processors. Curvelet's ability to extract complete spectrum information from an image with varying edge orientations makes it valuable for assessing data about wave propagation. In light of this, the curvelet transform is considered an excellent filter for improving and denoising images, and it is intended to deal with components in several orientations and scales. The primary objective of this treatment is to boost visibility and eliminate noise, which is superior to conventional techniques of enhancement in which the enhancement step is performed by discrete curvelet transform through wrapping. The contribution of the proposed review the usual vector is the second step of this review approach. The vector image is distributed randomly to each pixel in images that are unclear and noisy. This procedure eliminates the unpredictability in the previously computed directions and makes them regular, producing more precise results and enhancing image clarity, particularly at the image's edges. By comparing the results of the proposed technique of improvement to the results of existing improvement methods, it was determined that the proposed approach yields superior performance and is more efficient. The efficiency and efficacy of the suggested technique may be determined based on the results of the trials, which demonstrated that the method performs very well and yields accurate and precise results.
What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components

Explainability techniques for data-driven predictive models based on artificial intelligence and machine learning algorithms allow us to better understand the operation of such systems and help to hold them accountable (Sokol & Flach, 2021a).New transparency approaches are developed at breakneck speed, enabling us to peek inside these black boxes and interpret their decisions.Many of these techniques are introduced as monolithic tools, giving the impression of one-size-fits-all and end-to-end algorithms with limited customisability.Nevertheless, such approaches are often composed of multiple interchangeable modules that need to be tuned to the problem at hand to produce meaningful explanations (Sokol et al., 2019).This paper introduces a collection of hands-on training materials -slides, video recordings and Jupyter Notebooks -that provide guidance through the process of building and evaluating bespoke modular surrogate explainers for tabular data.These resources cover the three core building blocks of this technique: interpretable representation composition, data sampling and explanation generation (Sokol et al., 2019).
Certificateless aggregate signature-based carbon quota trading scheme

Carbon quota trading encourages enterprises to reduce carbon emissions by limiting their carbon emissions. However, carbon quota trading has some security problems, such as forgery, counterfeiting or tampering. Based on certificateless aggregate signature and smart contract technology, a secure carbon quota trading scheme is proposed. The use of certificateless aggregated signature technology ensures the integrity of carbon quota transactions, prevents transactions from being tampered with or forged, and avoids complex key management issues. The smart contract on the blockchain aggregates multiple single signatures to ensure the fairness of carbon quota trading. The analysis results show that the proposed scheme can protect the interests of enterprises of both sides of carbon quota trading and has higher computing and communication performance.
Design and implementation of a sign language recognition system based on convolutional neural networks (CNNs)

In this paper, we propose a sign language recognition system based on convolutional neural networks (CNNs) using static number gestures (from zero to seven). We collect images captured by a computer camera, using Python as the scripting language, to process the data on Open-Source Computer Vision Library (OpenCV). Keras Sequential model is used to construct a CNN-based model, and this model is then used to perform a series of functions, including the detection and recognition of sign language.
[A lightweight multiscale target object detection network for melanoma based on attention mechanism manipulation].

To propose a deep learning target detection model AM- YOLO that integrates coordinate attention and efficient attention mechanism.Mosaic image enhancement and MixUp mixed-class enhancement were used for image preprocessing. In the target detection model YOLOv5s with One-Stage structure and modified backbone network and neck network, the maximum pooling layer of the spatial pyramid of the backbone network was replaced with a two-dimensional maximum pooling layer, and the coordinate attention mechanism and the efficient channel attention mechanism were integrated into the C3 module and the backbone network of the model, respectively. The improved model was compared with the unmodified YOLOv5s model, YOLOv3 model, YOLOv3-SPP model, and YOLOv3-tiny model for relevant algorithmic indicators in comparative experiments.The AM-YOLO model incorporating coordinate attention and efficient channel attention mechanism effectively improved the accuracy of melanoma recognition with also a reduced size of the model weight. This model showed significantly better performance than other models in terms of precision, recall rate and mean average precision, and its mean average precision for benign and malignant melanoma reached 92.8% and 87.1%, respectively.The deep learning-based target object detection algorithm model can be applied in recognition of melanoma targets.
CoEPinKB: Evaluating Path Search Strategies in Knowledge Bases

A knowledge base, expressed using the Resource Description Framework (RDF), can be viewed as a graph whose nodes represent entities and whose edges denote relationships. The entity relatedness problem refers to the problem of discovering and understanding how two entities are related, directly or indirectly, that is, how they are connected by paths in a knowledge base. Strategies designed to solve the entity relatedness problem typically adopt an entity similarity measure to reduce the path search space and a path ranking measure to order and filter the list of paths returned. This article presents a framework, called CoEPinKB, that supports the empirical evaluation of such strategies. The proposed framework allows combining entity similarity and path ranking measures to generate different path search strategies. The main goals of this article are to describe the framework and present a performance evaluation of nine different path search strategies.
Prediksi Jumlah Pasien Covid-19 Dengan Menggunakan Klasifikasi Algoritma Machine Learning

Corona virus or servere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a disease that results in the occurrence of mild to moderate respiratory tract infections. Positive cases of Covid-19 in Indonesia were first detected on March 2, 2020 and continue until 2022. The additional number of deaths caused by COVID-19 has also increased. Therefore, the author is interested in making a predictive model of the cumulative number of COVID-19 patients who died in Indonesia. Therefore, in this study is how to predict the number of patients who die from COVID-19 in Indonesia by creating an appropriate accuracy model to help estimate the number of deaths associated with COVID-19 in Indonesia and assist the government in dealing with cases of new variants of COVID-19. In this study, the authors used the Decision Tree model using entropy criteria as well as Information Gain and Random Forest which resulted in accuracy rates of 91.83% (Decission Tree) and 73.80% (Random Forest). The results, explain that the model used is good. The more the R-squared error value is close to 1, the better the model used will be
ORIGINALITY VERSUS PLAGIARISM AND SIMILARITY IN ARCHITECTURE: COPYRIGHT ASPECTS OF ARCHITECTURAL DESIGN

Intellectual property protection via copyright in architecture has been a matter of much controversy. This stemmed from the complex nature of the architectural creative process and its often long duration. Here, there were accusations of the means and scope of copyright protection of architectural designs being insufficiently precise, especially with respect to the inalienability of the author–work link and the lack of economic balance between the designer and the client. The subject of the unintentional infringement on another’s intellectual property in architecture in the context of plagiarism itself.
Effect of facial expression in face biometry for a multimodal approach

With the advancement of technology, security has become an inseparable part of it. But many factors often influence the accuracy of the authentication system. In this current scenario, the multimodal biometric system is used where information from different modalities are fused to address the weakness of the system. In the present work, a robust biometric authentication system proposed using face and facial expression as biometric modalities. Facial recognition is the most commonly used biometric system over the years. Facial expressions of an individual are unique and it is integrated as an additional layer along with face recognition system to enhance the security of the system as the current scenario tends towards intelligent security systems for real-time surveillance. After pre-processing, eigenvalue-based and local binary pattern (LBP)-based features are extracted from the face and facial expression and the information are fused. Finally, the authentication is done using image Euclidian distance (IMED) based classifier. This proposed work evaluated using the JAFFE and Yale database and 95.71% and 88.89% authentication accuracy is achieved, respectively.
Human Boxing Motion Prediction Using Neural Networks

Numerous thoughts that were previously deemed inconceivable have become a reality as a result of decades of technical progress and improvement. While flying automobiles are still in the far future, artificial intelligence that can predict your next move is rapidly approaching. Human motion prediction is a relatively new area of active research that is interesting for it’s potential of improving robot’s and other machinery’s ability to work with human, such as passing objects to human, and avoiding crash into human, etc. This thesis focuses on predicting human boxing moves based on RGB visual input as an artificially intelligent boxing trainer with the help of recurrent neural networks (RNNs). I study and compares the performance of six distinct neural network architectures. I have method 1, which includes four model architectures taking 3D joint data as input, and method 2, which includes two architectures that take RGB image as input. Based on the results of all my research, I have discovered the most effective and efficient architecture for scenarios with sparse data based on the outcome of my study.
Prediction of Operation Time of Container Ship at Berth under Uncertain Factors Based on a Hybrid Model Combining PCA and ELM Optimized by IPSO

With the rapid development of global trade, the turnover of shipping containers has increased rapidly. How to use port resources reasonably and efficiently has become one of the main challenges that ports need to deal with when planning for the future. In order to develop scientific and efficient berth plans to improve operational efficiency and service level, this paper proposes a hybrid prediction model based on Principal Component Analysis (PCA) and Extreme Learning Machine (ELM) optimized by Improved Particle Swarm Optimization (IPSO), namely, the PCA-IPSO-ELM model. After assessing the uncertain factors influencing the operation time of the container ship at berth, this work reduces the dimensionality of the investigational data by the PCA method. Aiming to solve easy premature convergence of the traditional particle swarm algorithm, this paper introduces an improved particle swarm optimization algorithm via dynamic adjustment of nonlinear parameters. This improved particle swarm algorithm is mainly used to optimize the weights and thresholds of the extreme learning machine. Thus, a PCA-IPSO-ELM model which aims to forecast the operation time of a container ship at berth, is constructed. Using the historical operation data of the Tianjin Port Container Shipping Company as the prediction sample, this PCA-IPSO-ELM model is compared and assessed with traditional models. The results show that compared with other models, the PCA-IPSO-ELM prediction model has the characteristics of high prediction accuracy, fast running rate and strong stability, and it has a higher coefficient of determination and a better fitting degree.